Suchergebnisse

That's the title of a conference in Padova, September 21-22. Call for papers (deadline 5/31!). The COVID-19 pandemic and the 40-year high inflation rate have triggered massive spikes in risk and uncertainty and raised many challenges for macroeconomic modelers and forecasters. Against this background, the conference papers will cover, among others, topics such as various […]

Sept 21-22, at the University of Padua, organized by International Institute of Forecasters, University of Padua, American University, Federal Reserve Bank of Atlanta, and IMF. Day 1, Thursday September 21 Welcome address by Efrem Castelnuovo (University of Padova) Session 1 Moderator: Giovanni Caggiano (University of Padova) The Dynamic Nature of Macroeconomic Risks Sarah Mouabbi (Bank of […]

After the COVID-19 pandemic, criticism regarding the Federal Reserve's forecasting capabilities of economic variables has increased. From Wall Street CEOs like Jamie Dimon to think tanks like The Hoover Institution, the consensus has been that the Fed's human and institutional errors not only create inaccurate forecasts but that these forecasts lead to bad monetary policy. […]
The post Federal Reserve Economic Forecasts Better Than What People Think appeared first on The Lowe Down.

Presentation of paper by coauthor Laurent Ferrara and myself, today at International Symposium on Forecasting 2023… One picture (detail) to be shown: Figure 1: Reported year-on-year Industrial production (black), out-of-sample ex post simulation for term spread/short rate (blue), specification augmented with Chicago Fed NFCI and foreign term spread (red), and specification yet adding debt-service ratio […]

Biden is visiting Wisconsin. it's interesting to see how the state economy is doing, and what the Evers administration is forecasting (based primarily on national macro developments). First employment: Figure 1: Wisconsin nonfarm payroll employment (bold black), and Wisconsin Economic Forecast (blue), both in 000's, s.a. Source: BLS and Wisconsin Dept of Revenue Nov. Forecast. […]

A letter to the editor of the ASI blogDear Sir,I was interested to read the findings of Mr Benanke's independent review into the Bank of England's forecasting and related processes during times of significant uncertainty. It was a detailed analysis with all recommendations accepted by the Bank of England. Within this detail, was a list of shortcomings which focused on the deficiencies of the Bank's forecasting infrastructure. These included: out-of-date software, insufficient resources to ensure the software and models are adequately maintained, significant shortcomings in COMPASS (the baseline economic model), makeshift fixes, over-complication, and unwieldy systems to highlight a selection of Bernanke's findings. As I read this list of points, it struck me that the Bank of England via the Prudential Regulatory Authority (PRA), regulates some of our largest financial institutions.  If one of these institutions received similar points of note post a PRA review there would be consequences and certainly remedial action required. This has been a tough period for the MPC and this report will make difficult reading for the Bank of England. The Bank of England is owned and ultimately regulated by the UK Government, and I hope they will take a similarly direct and stringent view as they review the report and action plan to fix. We have been reminded this week, with the Post Office enquiry, that taking "an arm's length relationship" with wholly owned UK Government entities is not advisable.Charles White-ThomsonSenior Fellow Adam Smith Institute

Alan Reynolds


A paper on "Managing Disinflation" was recently presented at Chicago Booth by former Fed Governor Frederic Mishkin, and four distinguished co‐​authors. "There is no post‐​1950 precedent for a sizable central‐​bank induced disinflation," they concluded, "that does not entail substantial economic sacrifice [unemployment] or recession."[1] That was, of course, cheerleading for the familiar "Phillips Curve" theory, which claims low unemployment causes high inflation by raising wages, so high inflation can only be reduced by higher unemployment.







The Phillips Curve is the heart and soul of the Federal Reserve's forecasting model. It is the reason Fed Chair Powell keeps fretting about "tight labor markets" as the reason the FOMC can never stop pushing short‐​term interest rates above long‐​term rates until another "hard landing" pushes the unemployment rate above 4.5 percent.
According to the "Managing Disinflations" and endless lectures by Fed officials, the reduction of inflation since last June could not have happened. After 15 months in which the CPI inflation rate averaged 8.5 percent (and the fed funds rate was tiny), it has now fallen to 3.1 percent for 11 months. Did that happen because the unemployment rate went up? On the contrary, unemployment fell from 4.5 percent to 3.6 percent.
But the Fed and academic economists are not easily dissuaded by troublesome facts. They just keep on searching for new ways of explaining why the theory is still right, but the world has gone wrong.


[1] Stephen G. Cecchetti, Michael E. Feroli, Peter Hooper, Frederic S. Mishkin, and Kermit L. Schoenholtz. "Managing Disinflation" (February 2023) Table 2.1. https://​www​.chicago​b​ooth​.edu/​-​/​m​e​d​i​a​/​r​e​s​e​a​r​c​h​/​i​g​m​/​d​o​c​s​/​u​s​m​p​f​-​2​0​2​3​-​confe…

Marc Joffe


According to the United Nations, the world population surpassed the eight billion mark on November 15, 2022, and reached 8.045 billion on July 1 of this year. But these figures vary significantly from US Census Bureau estimates which placed the global population at just 7.979 billion at the beginning of July.
Population and especially population growth estimates are an important basis for policy. The UN projects that the population will continue to grow into the 2080s when it will reach 10.4 billion. Reacting to these figures, Liu Zhenmin, UN Under‐​Secretary‐​General for Economic and Social Affairs observed: "Rapid population growth makes eradicating poverty, combatting hunger and malnutrition, and increasing the coverage of health and education systems more difficult."
Writing in The Guardian, the newspaper's former environment editor John Vidal concluded "The hard fact is that in an age of climate breakdown, human numbers matter. And the ecological impact of another 2–3 billion humans will be immense." He went on to note that the Intergovernmental Panel on Climate Change had identified global population growth as one of the two biggest drivers of growing CO2 emissions, the other being increasing GDP per capita.
Concerns about climate change, hunger, and inadequate service provision may be mitigated if the world's population is growing more slowly and is on course to stagnate earlier than the UN expects.
A country‐​by‐​country comparison of the UN and US Census figures reveals some large discrepancies in both directions. The accompanying table shows all cases in which the respective estimates differed for a given country by five million people or more.











The UN's estimate for China is especially doubtful. China's official population estimate was 1,412 million at the end of 2022 reflecting an 850,000 person drop from the previous year. And the decline is likely to have accelerated in 2023 with demographers forecasting a lower number of births than in 2022 and perhaps a million excess deaths from COVID-19 following the termination of pandemic restrictions.
Chinese authorities have not been forthcoming about COVID deaths adding to suspicions that they are overstating the nation's population. One independent expert, Yi Fuxian of the University of Wisconsin‐​Madison estimates China's population at no more than 1.28 billion which is at least 130 million below the official figure. Fuxian offers evidence that the Chinese authorities have been systemically overstating births for decades.
He also casts doubt on the UN's estimate of India's population. That nation's last Census, in 2011, found 1,211 million people, or about five million less than the UN data show for that year. If fertility has plummeted more than the UN expected the gap between more recent estimates and reality will likely be magnified. We will only know for sure when India conducts its next Census in 2024.
While US Census estimates appear to be better than UN estimates for China and India, both are flawed in the case of the third most populous country, the United States. Both sources place US population at slightly under 340 million as of July 1, 2023 (for the US Census, I'm referring to its International Database data file; a lower number is shown on the Census Bureau's World Population Clock web page).
However, the US Census Bureau's July 1, 2022 domestic population estimate was 333 million, reflecting a 1.2 million increase over the prior year and a 1.9 million increase over the 2020 decennial Census. Even with the bounce‐​back in population growth post‐​pandemic, it is hard to imagine US population totaling more than 335 million as of July 1, 2023, suggesting a five‐​million‐​person overestimate in both global datasets.
So, there is good reason to believe that the global population did not exceed 8 billion last year and may still be below that milestone in late July 2023. Given the lower 2023 level and apparently slower growth in recent years, the UN's projection of a 10.4 billion peak in 2086 should also be called into question.
Other researchers have more modest growth expectations. The Institute for Health Metrics and Evaluation projects a peak population of 9.7 billion in 2064, while Earth4All expects a peak of less than 9 billion around 2050 with an even lower maximum if development in poorer nations accelerates.
Whether we should welcome slower population growth is open to debate. While the mainstream view focuses on the threats of overpopulation, Cato's Marian Tupy welcomes new people as a source of innovation. As Tupy wrote when the world population purportedly hit the 8 billion mark last November: "Every new human being comes to the world not only with an empty stomach, but also a pair of hands, and, more importantly, a brain capable of intelligent thought and new knowledge creation."
But regardless of whether one welcomes or fears population growth, the policy debate is impoverished by stale and inaccurate data. We often hear that we're entitled to our own opinions, but not our own facts (a quote often attributed to the late Senator Daniel Patrick Moynihan but which appears to have earlier origins). Yet in this case, the very foundational question of how many people now reside on earth, there is no reliable source of ground truth.

Perhaps it's merely a reflection of a fantasy, or
more seriously a last-gasp effort to stamp an agenda about to be washed entirely
out to sea, but playing like tomorrow doesn't exist Democrat Gov. John Bel Edwards announced
high-speed passenger rail service between Baton Rouge and New Orleans would
commence sometime in 2027.

Edwards said the state had agreed with Amtrak to
start this up with a once-daily roundtrip between the two cities. By then, presumably
an extensive upgrading of 80 miles of tracks will have been completed, supposedly
costing $250 million. The state would have to put up $50 million for the
upgrades.

Actually, it doesn't have to come up with just
under $20.5 million of that amount. Not long after the 2023 Regular Session of
the Legislature closed after allocating that amount of money to pay off the
federal government to zero out the 2005-era hurricane disaster Road Home
program, the federal government said the state could keep the money if it used
it for disaster preparation. Eschewing any alternative use, and to the displeasure
of some Republican legislators on the Joint
Legislative Committee on the Budget who had to approve reprogramming the
money who pointed out more cost-effective uses, the
Edwards Administration steered that towards the state match for high-speed
intercity passenger rail. The JLCB approved only because Edwards negotiated the
whole deal beforehand and then presented it as an accomplished fate that if not
approved would scuttle the swap.

However, Edwards didn't mention where the other
almost $30 million would come from, and that grant has yet to be awarded. Nor did
any discussion occur about if and when the upgrades happened how the state
would fund the ongoing enterprise, with Amtrak only saying its contribution would
be determined in the future.

That is a many millions of dollars question. A
study nearly a decade ago produced
some dismal numbers, forecasting a combined government subsidy of $44 a
ticket given ridership and total costs, including those amortizing an estimated
30 years of life for infrastructure – and based upon ridership and revenue numbers
historically optimistic and downplaying costs. Further, it would do little to
remove vehicle traffic from the corridor – not even a two percent reduction,
and that as well was an optimistic guess because data show typically a rail
service has to be at least 200 miles in length to become more cost effective than
driving. And for people without access to a car, Greyhound
currently advertises one-way bus service between $14-16 – all without
taxpayer subsidy and comparable to the predicted rail ticket price.

Edwards ignored all of this and prattled on with bogus
talking points about how much environmental degradation could be avoided
and how this would spur economic development – even though research into how intra-
and inter-city rail stimulates that shows this stimulation occurs only at far
higher passenger volumes than the contemplated 250-375 passenger round trips
a day, according to the latest
study from this year. In fact, for at least the first year Edwards
indicated only one round trip a day would happen, driving average daily ridership
down to fewer than 125.

The latest reports reiterates the fact that state
and local governments would have to kick in operating subsidies for this to
work. Disregarding the 30-year amortization costs, under the two roundtrips
forecast after expected federal subsidies cease in seven years after operations
start, state and local governments after a decade would be on the hook for $7
million annually, or almost $78 per ticket (this assumes ridership doesn't
increase and is a mixture of roundtrips from all points, not just from the
endpoints) or four times the average cost to a rider.

Further, the $250 million cost may be low. A scenario
for buildout just for the single round trip pegs the cost at $281.2 million. Go
all the way to four and it crests at $413.2 million (this assumes construction
actually begins in 2028 rather than next year, so inflation is built in, but at
3 percent this underestimates that, so these probably are pretty close forecasts
if building soon).

Because of the Edwards power play reprogramming
the seed money, the state may well have to come up with the remainder and do
the upgrades. There is one half-sensible reason to pursue that, one
increasingly fronted by Edwards to try to drown out the criticism of this as a
boondoggle – use of the rail corridor as an evacuation mechanism for New Orleans.
It would be an incredibly expensive one – much more than alternatives that JLCB
Republicans pointed out – but you can't put a price on lives.

Except it would seem to have limited utility as an
evacuation mechanism. The trip on high-speed rail would take without stop 75
minutes between endpoints, but less than half that to LaPlace, although of
course would require a trip back. And fewer than a dozen passenger cars typically
would be available. Of course, all hands could come on deck and additional locomotives
could be pressed into service and people loaded on freight box cars, but these
would have to move much more slowly. In sum, even if this took place over something
like an eight-hour span, it seems unlikely that more than a few thousand people
could be moved out of harm's way.

So, when Edwards alleged that "The costs are not
exorbitant, in terms of the benefit that's going to be delivered," even with
his track record of mendacity this was a bold unsupported assertion. And one
that shouldn't be allowed to be tested.

Because with Republican Atty. Gen. Jeff Landry ascending to the governorship
with Republican legislative supermajorities to back him up, they have every
means by which to stop this nonsense. Although some Republicans who have districts
along the route might be tempted to hit up taxpayers to put the boondoggle in
action, Landry should make clear to them now he will veto any state subsidy to
operating that passenger rail. If the federal government (perhaps as part of
efforts to restore
New Orleans to Mobile passenger service) and/or local governments want to
fund it, fine, but state taxpayers need not have their money wasted on such an extraordinarily
high cost, low benefit activity.

By announcing this now, Edwards may have thought he
could bum rush the state into that future commitment. Landry and Republicans
need to show him otherwise.

Muhammad Zia Mehmood is a PhD candidate in the Business and Public Policy Program at the Haas School of Business, UC Berkeley. In his job market paper, Zia studies the demand for and potential of business trainings provided over text messages to impact outcomes for micro-entrepreneurs in Kenya. This study was supported by CEGA's Development Economics Challenge initiative. This blog post was originally published on the Econ That Really Matters blog.A small business welder at work. Credit: Adobe StockIntroductionSmall businesses form the economic backbone of low-income countries and strengthening these enterprises is fundamental to alleviating poverty. Poor management practices is a major factor constraining firm productivity in these contexts, and over $1 billion is spent annually to address this constraint by providing business trainings to entrepreneurs. However, most of these are conventional, in-person, classroom-style trainings, which are expensive and hard to scale, and can exclude those who are unable to participate in person. Due to their low costs, scalability, and reach, phone-based trainings are gaining popularity as a potential solution, but there is limited evidence on whether remotely provided trainings are effective for micro-entrepreneurs in low-income settings.In my job market paper, I study the demand for and potential of text message-based business trainings using a field experiment in Kenya, in which access to an SMS-based training was randomized across 4,700 micro-entrepreneurs. I estimate short- and longer-run impacts using phone-based surveys conducted three months (Midline: 307 observations) and twelve months (Endline: 2,780 observations) after the intervention. I also elicit ex ante predictions for 12-month treatment effects from researchers through the Social Science Predictions Platform (SSPP), to assess whether the main findings depart from existing priors. Finally, I measure demand for the trainings through Take-It-Or-Leave-It (TIOLI) offers and the Becker-DeGroot-Marschak (BDM) willingness-to-pay elicitation method for a subset of the sample.Context and InterventionAccording to a 2016 nationwide survey of small businesses in Kenya, 90 percent of micro-entrepreneurs had never received any type of business training. This was reflected in their business practices: More than three-fourths did not advertise any of their products, over two-thirds didn't keep any business records, and less than a tenth accounted for prices set by their competitors when choosing their own prices.I partnered with a local firm specializing in digital content development and dissemination to implement an SMS-based training aimed at addressing these management gaps and others. Available in English and Swahili, the training modules covered best practices, including marketing, advertising, pricing, record-keeping, and stock management. The content was structured around stories about the decisions of hypothetical micro-entrepreneurs in different scenarios. Users accessed the trainings through self-paced engagement with an interactive chat-bot, which sent bite-sized chunks spanning about 150 text messages. The entire training could be completed in five to seven hours, and all content was retained indefinitely on users' phones. Weekly text reminders were sent to those who stopped engaging, and these reminders stopped if engagement was resumed or after two consecutive months of inactivity.The primary sample for the study was sourced from a list of micro-entrepreneurs compiled by my implementation partner and a local microfinance institution. Half of the study sample consists of female micro-entrepreneurs, and roughly 45 percent is based in rural areas. The average individual was about 35 years old, and had completed almost twelve years of education (high school level).Figure 1: Screenshots of user engagement with the chatbot. Note: This figure shows screenshots of interactions with the SMS-based chatbot as it pushes out content to users. In this context, most micro-entrepreneurs set prices just based on their buying costs, without accounting for prices of their competitors, so the content pushes them to change their pricing strategy. Credit: Muhammad Zia MehmoodResultsThree months after the intervention, I find that the SMS training increased knowledge and adoption of best practices by 0.20 and 0.33 standard deviations, respectively. I also find large positive, but statistically insignificant, effects on business performance in the overall sample, and significant positive effects for younger (below-median) micro-entrepreneurs on sales (109 percent increase), profits (38 percent increase), and business survival (11.6 percentage points increase). These positive effects for younger entrepreneurs are driven by higher engagement with the content, and larger effects on time spent on business, and loan amounts applied for and received.However, these positive results dissipate in the longer run; twelve months after the intervention, I see no effects on knowledge and adoption of best practices, as well as business sales, profits and survival. Additionally, the positive effects on business outcomes observed for younger entrepreneurs at three months disappear after twelve months. The lack of long-term impact was likely driven by micro-entrepreneurs abandoning all interactions with the content within the first few months of the intervention. The survival curve in Figure 2 shows how all cumulative aggregate engagement with the platform ended by May 2022 — five months into the intervention.Figure 2: Survival curve of interactions with chatbot. Note: This figure illustrates how interactions with the chatbot were distributed throughout the study period. The plot shows reverse cumulative engagement over time; for example, it shows that 80% of all the interactions with the chat-bot throughout the course of the study, had ended by 4/1/2022. The shaded areas represent the time-spans during which the Midline and Endline surveys were conducted. Credit: Muhammad Zia MehmoodFigure 3: Predictions vs observed treatment effects. Note: This figure shows how predicted treatment effects for the Endline compare with observed Midline and Endline effects. Error bars represent 90% confidence intervals. Credit: Muhammad Zia MehmoodFigure 3 illustrates how these results compare with predictions for 12-month treatment effects elicited ex ante through the SSPP. I find that SSPP researchers overestimated the engagement levels, both in terms of the proportion of the treatment group that would start engaging with the content (50 percent vs 30 percent) and how much training content the average user would complete after twelve months (40 percent vs 7 percent). Furthermore, predictions for the 12-month treatment effects on knowledge and adoption of best practices are somewhat similar to observed effects at three months, but significantly overestimated in light of observed 12-month treatment effects. Effects on business performance offer a similar story: SSPP predictions for the 12-month treatment effects on sales and profits are similar in magnitude to effects observed at three months (albeit statistically insignificant), but they grossly overestimate the effects at twelve months.Additionally, notwithstanding the low engagement and lack of longer-run effects, I find positive demand for SMS-based trainings among micro-entrepreneurs; both methods of elicitation — the TIOLI offers and the BDM exercise — reveal that individuals are willing to pay a small amount for an additional SMS-based training, suggesting that they value access to the content.Policy ImplicationsThese results indicate that SMS-based trainings are unlikely to improve outcomes for micro-entrepreneurs in the long run, despite their growing popularity in low-income and less accessible settings. These findings also highlight the lack of engagement with trainings as a major challenge that limits the potential of remotely-provided information-based support.Further, the forecasting exercise reveals that social science researchers overestimate the potential of SMS-based trainings to improve outcomes for micro-entrepreneurs, and the findings from this study are thus contrary to priors. Updating these priors is important because policymakers and practitioners often rely on social science experts to make decisions about how to invest in remote, information-based support programs.Lastly, the results on willingness to pay suggest that engagement with the content might not reflect the actual demand for SMS-based trainings, pointing towards possible behavioral drivers constraining engagement. To capitalize on the full potential of digital content delivery in low-income settings, further research is needed to shed light on how to encourage engagement with remotely provided content.Short Messages Fall Short for Micro-Entrepreneurs: Experimental Evidence from Kenya was originally published in CEGA on Medium, where people are continuing the conversation by highlighting and responding to this story.

The Phillips curve can mean one of two conceptually distinct things (which are sometimes confused). First, the Phillips curve may simply refer to a statistical property of the data--for example, what is the correlation between inflation and unemployment (either unconditionally, or controlling for a set of factors)? Second, the Phillips curve may refer to a theoretical mechanism--why does inflation and unemployment exhibit the statistical properties it does?

The presumption among many is that statistical Phillips curves tend to be negatively sloped, suggesting a trade-off between inflation and unemployment. A standard theoretical interpretation of this negative relationship is that a high level of unemployment means that aggregate demand is low, so that firms feel less inclined to increase the price of their goods and services. Conversely, when unemployment is low, aggregate demand is high, allowing firms to raise their prices at a faster rate.

The problem is that statistical Phillips curves are not always negatively sloped. In fact, sometimes they appear to be positively sloped. Over long periods of time, the data looks like a shotgun blast (i.e., zero correlation). In a recent empirical study, however, Blanchard (2016) claims that the Phillips curve is alive (though perhaps not so well) in the U.S. data. Among other things, he reports that:

Low unemployment still pushes inflation up; high unemployment pushes it down. 
The slope of the Phillips curve, i.e., the effect of the unemployment rate on inflation given expected inflation, has
substantially declined. But the decline dates back to the 1980s rather than to the crisis. There is no evidence of a
further decline during the crisis.

Some economists reason that the theoretical Phillips curve only appears flat these days because monetary policy is successfully keeping inflation close to target. If a central bank can hit its target inflation rate perfectly, then it's no surprise that measured fluctuations in unemployment will have no statistical relationship with inflation. There's probably something to this argument.

Whatever the explanation, it will have to account for what I think is an interesting asymmetry in the  statistical Phillips curve. In particular, the U.S. Phillips curve appears to be negatively sloped when unemployment is rising (as in a recession) and is either flat or even positively sloped when unemployment is falling (as in a recovery).

In what follows, I measure inflation as the monthly year-over-year change in the PCE, averaged at the quarterly frequency. The unemployment rate is the quarterly civilian unemployment rate. I look at U.S. data 1980:1 - 2019:1.  Here's what the data looks like.


I define "recession" as quarters in which the unemployment rate is trending up and "recovery" as quarters in which the unemployment rate is trending down. I divide the sample above into four recession-recovery subsamples. In effect, I plot the Phillips curve conditional on whether the unemployment rate is rising or falling. A full analysis should also control for monetary policy and inflation expectations, but I leave that for another day. Here is what I find.

Episode 1. Recession 1981:1 - 1982:4 and Recovery 1983:1 - 1990:2



Episode 2. Recession 1990:3 - 1992:3 and Recovery 1992:4 - 2000:4




 Episode 3. Recession 2001:1 - 2003:3 and Recovery 2003:4 - 2006:4











 Episode 4. Recession 2007:1 - 2009:4 and Recovery 2010:1 - 2019:1




So it seems that the Phillips curve is alive and well -- but only in recessionary periods. Recessions in the United States tend to be sharp and short-lived. The unemployment rate displays a well-known cyclical asymmetry (something that labor-market search theory accounts for in a natural way; e.g., see here). Whatever it is that drives the unemployment rate sharply higher seems to release a disinflationary force that is not immediately mitigated by monetary and fiscal policy.

At the same time, it seems that the Phillips curve is dead -- at least, once the dust has settled and the economy enters into its typical recovery and expansion phase. (Or does the Phillips curve only appear flat because monetary policy tends to tighten policy over the recovery phase?)

Policy Implications?

What does this imply about the conduct of monetary policy? Well, we have to be careful, of course. But to my eye, the evidence above suggests that the Fed need not worry about letting the unemployment rate decline as far as it wants during a period of economic expansion. The specter of a sharp spike in future inflation because unemployment is too low seems nowhere evident in the data (see also Bullard 2017). In addition, we do not know where the so-called "natural" rate of unemployment resides at any given point in time, assuming that such an object even exists.

In the present environment, I think one might even be inclined to let inflation fluctuate below the target rate--in other words, treat the target rate as a soft ceiling when the economy is expanding. Trying to induce inflation higher during an expansion phase seems strange (imprudent?) to me for a couple of reasons.

First, what is the point of purposely taking an action that could be construed as making the cost-of-living grow more rapidly over time? How is such an action to be justified, apart from fulfilling an apparent desire on the part of a small number of technocrats to maintain "credibility" of the "symmetric" inflation target? There may be ways to justify persistent inflation overshooting following a period of persistent undershooting (e.g., if the goal is price-level targeting). But the arguments I've heard made in this regard are probably too subtle to communicate effectively and persuasively. If so, then why not just let inflation fluctuate between 0-2%. It's not like we can measure it with precision in any case (a point former Vice Chair Stan Fischer was fond of repeating).

Second, modern day central banks were built for the purpose of keeping a lid on inflation--they were not built to promote it. The present projected trajectory of deficit-spending will almost surely, sooner or later (Japan notwithstanding), generate inflationary pressure. (If it doesn't, then please just keep cutting taxes and increasing spending.) So again, it seems that the Fed (and the U.S. economy) might be better served by viewing 2% inflation as a soft ceiling--something to defend only in the event that inflation begins to wander significantly and persistently away from 2% (or whatever number one has in mind) in normal times. Let the fiscal authority have the fiscal space it wants/needs as long as inflation remains low.

Recessions, when they hit, tend to appear suddenly and unpredictably. Forecasting the precise date of a recession is a mug's game. Estimating recession probabilities seems more art than science. Perhaps the best that monetary policy can do is to be prepared to act quickly and decisively when the unemployment rate starts rising rapidly. If recent history is a guide, a sharp recession is likely to release a strong disinflationary impulse (related theory paper). In the old days, we might have labeled this a "money demand shock." Today, it is more likely to be described as a "flight to safety shock"--i.e., the safety of U.S. dollars and Treasury securities. I don't think it's particularly helpful to say that high unemployment is causing low inflation--the direction of causality may working in the opposite direction (a high demand for money/debt is causing low inflation). But either way, the appropriate policy response likely entails an accommodating expansion in the supply of money/debt.

Remember when the Fed's most pressing policy concern was missing their 2% inflation target from below for most of the decade following the financial crisis of 2008-09?  The concern never failed to puzzle me in all my time at the St. Louis Fed. I once let out how I really felt:All those years I was expecting low inflation and low interest rates to make the political opposition to ever-higher deficits melt away. As I recall explaining to my colleagues at the time "Either we'll get the biggest free-lunch of all time (increased government spending and/or tax cuts) or we'll get inflation." The inflation was inevitable, to my of thinking. I just didn't know when it would return. I certainly did not see the point of encouraging it! Well, inflation returned. But not exactly for the reasons I was expecting. What happened? ShocksWhat happened was COVID-19 and the Russia-Ukraine war. These two shocks were large, disruptive, and persistent. A great many people died. Large parts of the economy were shut down with the hope of slowing the spread of the virus so as not to overwhelm our limited ICU capacity. The leisure and hospitality sector was crushed, and other sectors as well. There was a massive (and highly unusual) reallocation of production and consumption away from services to goods--a phenomenon that has not fully reversed to this day. We learned about the delicate and interconnected nature of global supply chains. People modified their behavior in dramatic ways. Work-from-home seems here to stay. And then, of course, as if a global pandemic was not enough, Russia invaded Ukraine in early 2022, leading to the usual sickening consequences of war: death, destruction, and displacement--as well as energy disruptions and food shortages that reverberated across the global economy. This is not, of course, the only thing that happened. We also had policy responses. Policy: What was neededI want to limit attention to economic policy here (health policy is another matter). The COVID-19 shock disrupted some sectors of the economy more than others. Some sectors, like leisure and hospitality were virtually shut down. But in many other parts of the economy, people were able to work from home. Since not many people purchased pandemic-insurance, a large number of Americans were in for a whole lot of economic hurt. Most of those adversely affected were in the bottom half of the income distribution. What could and should have been done?I should like to think that most Americans would have been in favor of a social insurance program that supported those most in need; i.e., targeted transfers for as long as the pandemic remained disruptive. Most people would have recognized that this is the right thing to do. And even those few who seemingly do not care much for their fellow Americans might have recognized how redistribution would have been desirable, perhaps even necessary, to maintain social cohesion. We should not have wanted a replay of what happened in the last crisis, where the financial sector was bailed out while American many households were largely left flailing in the foreclosure winds that blew in the aftermath of 2008-09. How might such a program be financed? A consumption tax would have been one way. Imagine a "transitory" 5% federal sales tax to fund a targeted transfer program. The program parameters could, in principle, be calibrated in a manner that requires little or no adjustment in the deficit. Ideally, such an emergency program would have already been put in place. (As far as I know, there is still no such plan in place--a significant policy failure, in my view.)How might things have
played out with such a policy, given the sequence of shocks that unfolded? To a
first approximation, my guess is "probably not much different." With the balanced-budget
policy described above, inflation would have almost surely been lower. Imagine
shaving 300-500bp off the "inflation hump" we've experienced so far:

We would almost surely still have had some inflation stemming from supply disruptions and energy
costs (associated with the war). But inflation would have been less pronounced. Naturally, rather than complaining about high inflation, people would instead have
been complaining about high consumption taxes. ("They told us they'd be
transitory!") There's no such thing as a free lunch. 

Under this higher-tax/lower-deficit policy, most Americans would have felt worse off relative to
2019. The blame for this feeling, however, properly lies with the shocks and
not the policy response. Yes, work-from-home types would not have received
transfers and they would have been paying more for goods and services. This is
the nature of redistribution, which I believe most people would have supported. Policy: What we gotTo a large extent--and all things considered--we pretty much got what was needed: a set of redistributive policies with transfers targeted (mostly) to the bottom half of the income distribution (yes, yes, we can talk at length about how things could have been done better). Except that there was no surtax to fund the transfers. Our representatives in Congress chose to deficit-finance the programs. The resulting large quantity of treasury paper had to be absorbed by the private sector at a time supply was constrained and interest rates were not permitted to rise (I'll get to monetary policy in a moment). How does one not expect some additional inflation in this case? So, instead of a "transitory" consumption tax, we got a "transitory" inflation tax. There's no free lunch. By the way, by "transitory" I mean to say that inflation is expected to revert to target, instead of remaining elevated or even increasing. In the fall of 2020, I expected a "temporary" inflation (see here) because I thought the supply disruptions and CARES Act were not permanent. Inflation turned out to be higher and more persistent than I expected. But the supply disruptions have largely alleviated and the ARP expired at the end of 2021 (though the RUS-UKR war continues). Up until recently, I remained optimistic that--absent further shocks and with responsible fiscal policy--inflation would make its way back down to target in 3-5 years without a recession. I'm not as optimistic today, but let me return to this below. What about monetary policy? Well, I was very pleased with the way the Fed calmed financial markets in March 2020, as I expected it would.Well done, Fed. But what about monetary (interest rate) policy?Well, to be honest, monetary policy seemed a bit bonkers. Lowering the policy rate in response to recession engineered by a manufactured shutdown did not make much sense to me. My view was more in line with Michael Woodford's, as expressed here in his 2020 Jean Monnet lecture. What was needed was insurance, not stimulus. And this insurance needs to be delivered through fiscal policy. My own view is that many economists could not resist interpreting the severe decline in output as reflecting a conventional "output gap." To be fair, there may very well have been a decline in aggregate demand in the first half of 2020. The economic outlook at the time was very uncertain, which likely increased the desire for precautionary savings. Remember, monthly inflation rates for March, April and May of 2020 were negative. The monthly inflation rate only became positive in June 2020 (5.4% annualized rate), though it remained fairly subdued for most of  2020. Heading into 2020, the Fed's policy rate was around 1.6%. Was it really necessary to lower it any further? Especially in light of the fiscal transfers taking place throughout 2020?  But apparently, in the minds of some, perhaps even most, the economy needed "stimulating." In any case, it seems clear now, in retrospect at least, that the cut should probably not have happened or, conditional on happening, should have been quickly reversed once the financial panic had subsided. The main effect of interest rate policy according to many was an undesirable asset-price boom (stocks, bonds, and real estate). The increase in private sector wealth coming from higher asset valuations surely added some fuel to the inflationary fire.  We can now see how that Fed-induced wealth effect is being undone. The rapidity of the rise in the Fed's policy rate is wreaking havoc on wealth portfolios. This is not a huge concern to the extent the policy is just reversing an undesirable asset-price inflation. But to the extent that these assets sit on bank balance sheets, to the extent these positions are not hedged against duration risk, to the extent that depositors are skittish, and to the extent that capital buffers are running low, then the banking system--or at least parts of it--are subject to runs. We are seeing this play out now in the United States. Where are we heading? I fear we may be in a bit of a pickle. One reason is China. To be more precise, the risk of the U.S. entering a long and costly proxy war with China. Let's hope it doesn't happen. But I can't help thinking of Rome vs. Persia. I'm not sure about the Persian perspective, but my reading of history suggests that the late Roman Empire devoted considerable resources to defending its eastern frontier against its great rival. Such a fiscal strain requires taxes (or inflation).  If the Sino-American proxy war scenario fails to materialize, then I think we stand a reasonable chance of getting out of this decade without a recession, but with inflation hovering above target for the indefinite future. The Fed might want to sell this as part of its "symmetric" inflation targeting regime. After all, we tolerated undershooting the target inflation rate for a decade (see here). In my view, much will depend on the course of fiscal policy--the deficit, in particular--in relation to the global demand for U.S. Treasury securities (see here). Needless to say, these are very difficult objects to forecast. (In fact, there's no point in forecasting them -- we should just make contingency plans instead.)There is a chance that the Fed overdoes its policy tightening and starts to "break things." Given the recent events in the U.S. banking sector, the FOMC would, in my view, be wise to pause and see how things play out. This is not an issue of "financial dominance." It is based on the deflationary impulse induced by the recent bank failures. I expect all banks to redouble their efforts to repair their balance sheets. This means a fear-induced tightening of lending standards and slower loan growth beyond what one might consider to be a normal reaction against higher policy interest rates.  If the Fed does pull a Paul Volcker, then we'll get a sharp recession. Inflation will come down--temporarily, at least. Where inflation goes from there will depend, as always (in my view), on fiscal policy.  If the proxy war scenario does come to pass, then get ready to pay the necessary taxes. And remember: wars are typically inflationary. In fact, an inflation tax may not be a bad way to finance a part of this endeavor. The U.S. would effectively be collecting a greater amount of seigniorage on its U.S. Treasury securities held abroad. And why shouldn't our allies be prepared to shoulder some of the expense? (There are other ways, of course.) A proxy war may or may not be worth fighting. Either way, remember: there ain't no such thing as free lunch.  As for monetary policy in a period in which the government has a set objective and wants to deficit-finance its spending, I'm afraid the Fed will just have to learn how to stop worrying and "love" inflation (in case you're unfamiliar with the reference, see here). Raising interest rates sharply can break things and create disinflation. But without fiscal reform, the respite on inflation is likely to be temporary. In fact, inflation is likely to reemerge even higher than before since the Treasury will now have to issue paper at an even faster pace, first, to cover the shortfall created by the recession, and second, to cover the higher interest expense of the debt. This is a version of Sargent and  Wallace's "unpleasant monetarist arithmetic," see here and here. Need I add that creating a recession is no way to win a proxy war.  How will U.S. policy evolve to meet our many challenges? No one knows how the future will unfold. Perhaps we can take some comfort in Winston Churchill's observation: "You can always count on the Americans to do the right thing--but only after they've tried everything else." Alas, the quote is apocryphal. Nevertheless, I am hopeful that we will "do the right thing" eventually (and before it's too late). 

Inflation is back together with a new season of America's favorite sport: zero-contact, finger-pointing. I thought I'd sit back and share a few thoughts with you on the subject on this cold Saturday afternoon. Use the comments section below to let me know what you think.In one corner, I see some pundits somehow wanting to blame the 2021 inflation on workers. Workers are somehow forcing their improved bargaining positions on employers, raising the costs of production, with some or all of these costs passed on to consumers. Then, as workers see their real wages erode, the cycle begins anew begetting the dreaded "wage-price spiral." Those pesky workers. There's no doubt something to the idea that wage demands can lead to higher prices (and why shouldn't workers want cost-of-living adjustments?) But what is the evidence that this behavior was the impulse behind the 2021 inflation? While it's difficult to tell just by eye-balling the data, I think it's reasonable (under this hypothesis) to see wage growth precede (or at least be coincident with) inflation. Unfortunately (for this hypothesis), this is not what we see in the data. In the diagram below, use the Atlanta Fed's Wage Growth Tracker to construct nominal wage inflation for the bottom (green) and top (yellow) wage quintiles. This is plotted against CPI inflation (blue). Another problem for this hypothesis is that wage inflation is moving in the wrong direction for the top three wage quintiles over the Covid era. What we see here is a clear acceleration in the rate of inflation, followed by modest acceleration in wage inflation for the bottom quintile and a deceleration in wage inflation for the top quintile. In 2021, real wages across all quintiles declined (according to this data). So much for increased worker bargaining power. [Note: it is quite likely that net income for the bottom one or two quintiles increased, thanks to government transfers.] On the other side of the political spectrum, we see pundits and politicians blaming the 2021 inflation on "corporate greed." Framing the issue in terms of "corporate greed" is not especially helpful, in my humble opinion. The substantive part of this claim is that large firms were somehow able to leverage their pricing power in 2021 into higher profit margins and record corporate profits. There is, in fact, some evidence in support of this. The diagram below plots profit margins for firms in the Compustat database. Profit margin below is computed on an after tax basis (net income divided by sales). The data is divided between large and not-large firms. Large firms are those in the top 10% of sales volume. By this measure, profit margins seem remarkably stationary over long periods of time. There is some evidence of a modest secular increase in margins c. 2003. Large firms have higher margins. But the part I want to focus on here is near the end of the sample. Profit margins for 90% of firms seem close to their historical average. We see some evidence that profit margins for the top 10% of firms increased in 2021. But this increase peaked in Q3 and then declined back to historical norms in Q4. While the spike in profit-margins likely contributed to inflation, it hardly seems like a smoking gun. And the Q4 reversion to the mean suggests that "corporate greed" is not likely to be a source of inflationary pressure in 2022. Well, if workers and firms are not to blame, then who or what is left? There's the C-19 shock itself, of course, along with the effects it has had on the global supply chain. But the 19 in C-19 refers to the year 2019 (and 2020). We're talking about 2022 here. Sure, the supply chain issues are still with us. But at most, I think they account for a substantial change in relative prices (goods becoming more expensive than services) and an increase in the cost-of-living (an increase in the price-level--not a persistent increase in the rate of growth of the price-level). While the factors above no doubt contributed in some way to the 2021 inflation dynamic, let's face it--the size and persistence of the inflation was mainly policy-induced. The smoking gun here seems to be the sequence of the C-19 fiscal transfers. As we know, this had the unusual and remarkable effect of increasing personal disposable income throughout most of the pandemic. The Fed also had a role to play here because it accommodated the fiscal stimulus (normally, one might have expected a degree of monetary policy tightening to partially off-set the inflationary impulse of fiscal stimulus). Below I plot retail sales (actual vs trend) and the timing of the fiscal actions. I used retail sales here (I think I got this from Jason Furman), but the picture looks qualitatively similar using PCE (the path of nominal PCE went above trend in 2021 and not earlier in the way retail sales did). Just eye-balling the data above, I'd say the CARES Act was a major success (especially under the circumstances). The subsequent two programs might have been scaled back a bit and/or targeted in a more efficient manner. And, knowing what we know now, the Fed could have started its tightening cycle in 2021. Having said this, I wouldn't go so far as to say these were flagrant policy mistakes--given the circumstances. If there was a policy mistake, it was in not having a well-defined state-contingent policy beforehand equipped to deal with a global pandemic. Not having that plan in place beforehand, I think monetary and fiscal policy reacted reasonably well.Policymaking in real-time is hard. And policy, whether formulated beforehand or not, must necessarily balance risks. There was a risk of undershooting the support directed to households. We saw this during the foreclosure crisis a decade ago. And there was a risk of overdoing it in some manner. Keep in mind that it was not clear when the legislation was passed how 2021 would unfold. Similarly, for the Fed--perhaps still feeling the sting of having moved too soon and too fast in the past, hopeful that inflation would decline later in the year--delayed its tightening cycle to 2022. It wasn't perfect. But taken together, the economic policy responses had their intended effect of redistributing income to those who suffered disproportionate economic harm during the pandemic. Finally, what does all this mean for inflation going forward? Well, as I suggested above, I don't think we have to worry about a wage-price spiral (the fiscal policy I think is necessary to support such a phenomenon is not likely to be present). Profit margins appear to be declining (reverting to their long-run averages). The money transfers associated with the last fiscal package are gone for 2022. No big spending bills seem likely to pass in 2022. For better or worse, we're talking a considerable amount of "fiscal drag" here (although, some have pointed to how flush state government coffers are at the moment). Hopefully (fingers crossed), supply-chain problems will continue to be solved. If so, then all of this points to disinflation (a decline in the rate of inflation) going forward. Some recent promising signs as well: [1] month-over-month CPI inflation has declined for two consecutive months (November and December); and [2] the ECI (employment cost index) decelerated in Q4 of 2021. (These numbers are notoriously volatile, so don't put too much stock in the direction. But still, it's better than seeing them go the other way.)Some caveats are in order, of course. In December 2020, I suggested we prepare for a "temporary" burst of inflation in 2021. While this came to pass, the level of inflation surprised me (to be fair, I hadn't incorporated the ARP in my assessment, but even if I had, I think I still would have been surprised). Moreover, I was also surprised by the persistence of inflation--I thought it would decelerate more rapidly (even given the ARP). This just serves to remind me how bad I am at forecasting. Someone recently mentioned a great quote by Rudi Dornbusch: "In economics, things take longer to happen than you think they will, and then they happen faster you thought they could." I can relate to this. Inflation may turn out to be more persistent that I am suggesting. But how might this happen, given the disinflationary forces I cited above? One reason may have to do with the tremendous increase in outside assets the private sector has been compelled to absorb--the increase in the national debt has manifested itself as an increase in private sector wealth. Jason Furman sees this as "excess saving." The question going forward is whether the private sector will be compelled to spend this (nominal) wealth (it already has done so, as my chart above shows) or continue to save (not spend) it? It is possible that this "pent up demand" will be spent over a prolonged period of time. The effect of this would be to keep inflation elevated higher than it would otherwise be (serving to reduce real nominal wealth). How long this might take, I have no idea. But even so, it seems clear that the effect cannot persist indefinitely. At some point the debt-to-GDP ratio will decline to its equilibrium position (D/Y has already started to decline; see here). Another reason why inflation forecasts should be discounted is that it's very difficult to forecast future contingencies. What might happen, for example, if Russia invades the Ukraine this year? Events like these will create disruptions and there's not much monetary and fiscal policy can do about them. But whatever happens, I think the long-run fiscal position of the U.S. will remain anchored (Americans will demand this). And remember, the Fed is bound by its Congressional mandates to keep inflation "low and stable." The Fed's record on inflation since the Volcker years has been pretty good. I'm betting that the record will be equally good over the next 10 years.***PS. I see some people out there strongly asserting it is a "fact" that fiscal policy did not cause the 2021 inflation (see here, for example). The reason, evidently, is because inflation is a global phenomenon. There's something to this, of course. After all, C-19 is a global pandemic. But this reasoning nevertheless seems faulty to me. First, the USD is the global reserve currency. It's quite possible that the U.S. exported some of its inflation to the world (much in the way it did in the 1970s). Second, many other countries (like Canada, for example) adopted similar fiscal policies. Those countries with less expansive fiscal policies also displayed lower inflation, as far as I know. Rather than deflect the blame, we should own it here. Fiscal policy had a lot of positive effects too (e.g., lowering child poverty). The challenge, as always, is to develop ways to calibrate these policies in a more effective manner. 

I just finished a new draft of "Expectations and the neutrality of interest rates," which includes some ruminations on inflation that may be of interest to blog readers. A central point of the paper is to ask whether and how higher interest rates lower inflation, without a change in fiscal policy. That's intellectually interesting, answering what the Fed can do on its own. It's also a relevant policy question. If the Fed raises rates, that raises interest costs on the debt. What if Congress refuses to tighten to pay those higher interest costs? Well, to avoid a transversality condition violation (debt that grows forever) we get more inflation, to devalue outstanding debt. That's a hard nut to avoid.  But my point today is some intuition questions that come along the way. An implicit point: The math of today's macro is actually pretty easy. Telling the story behind the math, interpreting the math, making it useful for policy, is much harder. 1. The Phillips curveThe Phillips curve is central to how the Fed and most policy analysts think about inflation. In words, inflation is related to expected future inflation and by some measure if economic tightness, factor \(x\). In equations, \[ \pi_t = E_t \pi_{t+1} + \kappa x_t.\] Here \(x_t\) represents the output gap (how much output is above or below potential output), measures of labor market tightness like unemployment (with a negative sign), or labor costs. (Fed Governor Chris Waller has a great speech on the Phillips curve, with a nice short clear explanation. There are lots of academic explanations of course, but this is how a sharp sitting member of the FOMC thinks, which is what we want to understand. BTW, Waller gave an even better speech on climate and the Fed. Go Chris!)  So how does the Fed change inflation? In most analysis, the Fed raises interest rates; higher interest rates cool down the economy lowering factor x; that pushes inflation down. But does the equation really say that? This intuition thinks of the Phillips curve as a causal relation, from right to left. Lower \(x\) causes lower inflation. That's not so obvious. In one story, the Phillips curve represents how firms set prices, given their expectation of other's prices and costs. But in another story, aggregate demand raises prices, and that causes firms to hire more (Chris Waller emphasized these stories). This reading may help to digest an otherwise puzzling question: Why are the Fed and its watchers so obsessed with labor markets? This inflation certainly didn't start in labor markets, so why put so much weight on causing a bit of labor market slack? Well, if you read the Phillips curve from right to left, that looks like the one lever you have. Still, since inflation clearly came from left to right, we still should put more emphasis in curing it that way. 2. Adjustment to equilibrium vs. equilibrium dynamics. But does the story work? Lower \(x_t\) lowers inflation \(\pi_t\) relative to expected future inflation \(E_t \pi_{t+1}\). Thus, it describes inflation that is rising over time.  This does not seem at all what the intuition wants. So how do we get to the intuition that lower \(x_t\) leads to inflation got goes down over time?  (This is on p. 16 of the paper by the way.) An obvious answer is adaptive expectations: \(E_t \pi_{t+1} = \pi_{t-1}\).  Then lower \(x_t\) does mean inflation today lower than it was in the past. But the Fed and most commenters really don't want to go there. Expectations may not be "rational," and in most commentary they are either "anchored" by faith in the Fed, or  driven by some third force. But they aren't mechanically last year's inflation. If they were, we would need much higher interest rates to get real interest rates above zero. Perhaps the intuition comes from remembering these adaptive expectations dynamics, and not realizing that the new view that expectations are forward looking, even if not rational, undermines those dynamics. Another answer  may be confusion between adjustment to equilibrium and movement of equilibrium inflation over time. Lower \(x_t\) means lower inflation \(\pi_t\) than would otherwise be the case. But that  reduction is an adjustment to equilibrium. It's not how inflation we observe -- by definition, equilibrium inflation -- evolves over time. This is, I think, a common confusion. It's not always wrong. In some cases, adjustment to equilibrium does describe how an equilibrium quantity changes, and in a more complex model that adjustment plays out as a movement over time. For example, a preference or technology shock might give a sudden increase in capital; add adjustment costs and capital increases slowly over time. A fiscal shock or money supply shock gives a sudden increase in the price level; add sticky prices and you get a slow increase in the price level over time. But we already have sticky prices. This is supposed to be the model, the dynamic model, not a simplified model. Here, inflation lower than it otherwise would be is not the same thing as inflation that goes down slowly over time. It's just a misreading of equations. Another possibility is that verbal intuition refers to the future, \[ E_t \pi_{t+1} = E_t \pi_{t+2} + \kappa E_t x_{t+1} .\]Now, perhaps, raising interest rates today lowers future factor x, which then lowers future inflation \(E_t\pi_{t+1}\) relative to today's inflation \(\pi_t\). That's still a stretch however. First, the standard new-keynesian model does not have such a delay. \[x_t = E_t x_{t+1} - \sigma(i_t - E_t \pi_{t+1})\]says that higher interest rates also immediately lower output, and lower output relative to future output. Higher interest rates also raise output growth. This one is more amenable to adding frictions -- habits, capital accumulation, and so forth -- but the benchmark model not only does not have long and variable lags, it doesn't have any lags at all.  Second, maybe we lower inflation \(\pi_{t+1}\) relative to its value \(\pi_t\), in equilibrium, but we still have inflation growing from \(t+1\) to \( t+2\). We do not have inflation gently declining over time, which the intuition wants. We are left -- and this is some of the point of my paper -- with a quandary. Where is a model in which higher interest rates lead to inflation that goes down over time? (And, reiterating the point of the paper, without implicitly assuming that fiscal policy comes to the rescue.) 3. Fisherian intuitionA famous economist, who thinks largely in the ISLM tradition, once asked me to explain in simple terms just how higher interest rates might raise inflation. Strip away all price stickiness to make it simple, still, the Fed raises interest rates and... now what? Sure point to the equation \( i_t = r + E_t\pi_{t+1} \) but what's the story? How would you explain this to an undergraduate or MBA class?  I fumbled a bit, and it took me a good week or so to come up with the answer. From p. 15 of the paper, First,  consider the full consumer first-order condition \[x_t = E_t x_{t+1} - \sigma(i_t -E_t \pi_{t+1})\] with no pricing frictions.  Raise the nominal interest rate \(i_t\).  Before prices change, a higher nominal interest rate is a higher real rate, and induces people  to demand less today \(x_t\) and more next period \(x_{t+1}\).  That change in demand pushes down the price level today \(p_t\) and hence current inflation \(\pi_t = p_t - p_{t-1}\), and it pushes up  the expected price level next period \(p_{t+1}\) and thus expected future inflation \(\pi_{t+1}=p_{t+1}-p_t\). So, standard intuition is correct, and refers to a force that can lower current inflation. Fisherian intuition is correct too, and refers to a natural force that can raise expected future inflation. But which is it, lower \(p_t\) or higher \(p_{t+1}\)? This consumer first-order condition, capturing an  intertemporal substitution effect, cannot tell us. Unexpected inflation and the overall price level is determined by a wealth effect. If we pair the higher interest rate with no change in surpluses, and thus no wealth effect, then the initial price level \(p_t\) does not change [there is no devaluation of outstanding debt] and the entire effect of higher interest rates is a rise in \(p_{t+1}\).  A concurrent rise in expected surpluses leads to a lower price level \(p_t\) and less current inflation \(\pi_t\). Thus in this context standard intuition also implicitly assumes that fiscal policy acts in concert with monetary policy. In both these stories, notice how much intuition depends on describing how equilibrium forms. It's not rigorous. Walrasian equilibrium is just that, and does not come with a price adjustment process. It's a fixed point, the prices that clear markets, period. But believing and understanding how a model works needs some sort of equilibrium formation story. 4. Adaptive vs. rational expectations The distinction between rational, or at least forward-looking and adaptive or backward-looking expectations is central to how the economy behaves. That's a central point of the paper.  It would seem easy to test, but I realize it's not. Writing in May 2022, I thought about adaptive (backward-looking) and rational (forward-looking), and among other points that under adaptive expectations we need nominal interest rates above current inflation  -- i.e. much higher -- to imply real interest rates, while that isn't necessarily true with forward-looking expectations. You might be tempted to test for rational expectations, or look at surveys to pronounce them "rational" vs. "behavioral," a constant temptation. I realize now it's not so easy (p. 44): Expectations may seem adaptive.  Expectations must always be, in equilibrium, functions of variables that people observe, and likely weighted to past inflation. The point of "rational expectations'' is that those forecasting rules are likely to change as soon as a policy maker changes policy rules, as Lucas  famously pointed out in his "Critique."  Adaptive expectations may even be model-consistent [expectations of the model equal expectations in the model] until you change the model.That observation is important in the current policy debate. The proposition that interest rates must be higher than current inflation in order to lower inflation assumes that expected inflation equals current inflation -- the simple one-period lagged adaptive expectations that I have specified here. Through 2021-2022, market and survey expectations were much lower than current (year on year) inflation. Perhaps that means that markets and surveys have rational expectations: Output is temporarily higher than the somewhat reduced post-pandemic potential, so inflation is higher than expected future inflation (\(\pi_t = E_t \pi_{t+1} + \kappa x_t\)). But that observation could also mean that inflation expectations are a long slow-moving average of lagged inflation, just as Friedman speculated in 1968 (\(\pi^e_t = \sum_{j=1}^\infty \alpha_j \pi_{t-j}\)). In either case, expected inflation is much lower than current inflation, and interest rates only need to be higher than that low expectation to reduce inflation. Tests are hard, and you can't just look at in-sample expectations to proclaim them rational or not. Rational expectations change when policy deviates from a rule, or when the policy rule changes. That's their key feature. We should talk perhaps about rational vs. exogenous expectations. 5. A few final Phillips curve potshotsIt is still a bit weird that so much commentary is so focused on the labor market to judge pressure on inflation. This inflation did not come from the labor market! Some of this labor market focus makes sense in the new-Keynesian interpretation of the Phillips curve: Firms set prices based on expected future prices of their competitors and marginal costs, which are largely labor costs. That echoes the 1960s "cost push" view of inflation (as opposed to its nemesis "demand pull" inflation). But it begs the question, well, why are labor costs going up? The link from interest rates to wages is about as direct as the link from interest rates to pries. This inflation did not come from labor costs, maybe we should fix the actual problem? Put another way, the Phillips curve is not a model. It is part of a model, and lots of equations have inflation in them. Maybe our focus should be elsewhere. Back to Chris Waller, whose speech seems to me to capture well sophisticated thinking at the Fed.  Waller points out how unreliable the Phillips curve is What do economic data tell us about this relationship? We all know that if you simply plot inflation against the unemployment rate over the past 50 years, you get a blob. There does not appear to be any statistically significant correlation between the two series.In more recent years, since unemployment went up and down but inflation didn't go far, the Phillips curve seemed "flat," the Phillips curve was very flat for the 20-plus years before the pandemic, You can see this in the decline of unemployment through 2020, as marked, with no change in inflation. Then, unemployment surged in 2021, again with no deflation.  2009 was the last time there was any slope at all to the Phillips curve. But is it "flat" -- a stable, exploitable, flat relationship -- or is it just a stretched out "blob", two series with no stable relationship, one of which just got stable? In any case, as unemployment went back down to 3.5 percent in 2022, inflation surged. You can forgive the Fed a bit: We had 3.5% unemployment with no inflation in 2020, why should we worry about 3.5% unemployment in 2022? I think the answer is, because inflation is driven by a whole lot more than unemployment -- stop focusing on labor markets! A flat curve, if it is a curve, is depressing news:  Based on the flatness of the Phillips curve in recent decades, some commentators argued that unemployment would have to rise dramatically to bring inflation back down to 2 percent. At best, we retrace the curve back to 2021 unemployment. But (I'll keep harping on this), note the focus on the error-free Phillips curve as if it is the entire economic model. Waller views the new Phillips curve as a "curve," that has become steeper, and cites confirming evidence that prices are changing more often and thus becoming more flexible.   ... considering the data for 2021... the Phillips curve suddenly looked relatively steep.. since January 2022, the Phillips curve is essentially vertical: The unemployment rate has hovered around 3.6 percent, and inflation has varied from 7 percent (in June) to 5.3 percent (in December).Waller concludes A steep Phillips curve means inflation can be brought down quickly with relatively little pain in terms of higher unemployment. Recent data are consistent with this story.Isn't that nice -- from horizontal to vertical all on its own, and in the latest data points inflation going straight down. Still, perhaps the right answer is that this is still a cloud of coincidence and not the central, causal, structural relationship with which to think about how interest rates affect inflation. If only I had a better model of inflation dynamics...

(This post continues part 1 which just looked at the data. Part 3 on theory is here) When the Fed raises interest rates, how does inflation respond? Are there "long and variable lags" to inflation and output?  There is a standard story: The Fed raises interest rates; inflation is sticky so real interest rates (interest rate - inflation) rise; higher real interest rates lower output and employment; the softer economy pushes inflation down. Each of these is a lagged effect. But despite 40 years of effort, theory struggles to substantiate that story (next post), it's had to see in the data (last post), and the empirical work is ephemeral -- this post.  The vector autoregression and related local projection are today the standard empirical tools to address how monetary policy affects the economy, and have been since Chris Sims' great work in the 1970s. (See Larry Christiano's review.) I am losing faith in the method and results. We need to find new ways to learn about the effects of monetary policy. This post expands on some thoughts on this topic in "Expectations and the Neutrality of Interest Rates," several of my papers from the 1990s* and excellent recent reviews from Valerie Ramey and  Emi Nakamura and Jón Steinsson, who  eloquently summarize the hard identification and computation troubles of contemporary empirical work.Maybe popular wisdom is right, and economics just has to catch up. Perhaps we will. But a popular belief that does not have solid scientific theory and empirical backing, despite a 40 year effort for models and data that will provide the desired answer, must be a bit less trustworthy than one that does have such foundations. Practical people should consider that the Fed may be less powerful than traditionally thought, and that its interest rate policy has different effects than commonly thought. Whether and under what conditions high interest rates lower inflation, whether they do so with long and variable but nonetheless predictable and exploitable lags, is much less certain than you think. Here is a replication of one of the most famous monetary VARs, Christiano Eichenbaum and Evans 1999, from Valerie Ramey's 2016 review: Fig. 1 Christiano et al. (1999) identification. 1965m1–1995m6 full specification: solid black lines; 1983m1–2007m12 full specification: short dashed blue (dark gray in the print version) lines; 1983m1–2007m12, omits money and reserves: long-dashed red (gray in the print version) lines. Light gray bands are 90% confidence bands. Source: Ramey 2016.  Months on x axis. The black lines plot the original specification. The top left panel plots the path of the Federal Funds rate after the Fed unexpectedly raises the interest rate. The funds rate goes up, but only for 6 months or so. Industrial production goes down and unemployment goes up, peaking at month 20. The figure plots the level of the CPI, so inflation is the slope of the lower right hand panel. You see inflation goes the "wrong" way, up, for about 6 months, and then gently declines. Interest rates indeed seem to affect the economy with long lags. This was the broad outline of consensus empirical estimates for many years. It is common to many other studies, and it is consistent with the beliefs of policy makers and analysts. It's pretty much what Friedman (1968) told us to expect. Getting contemporary models to produce something like this is much harder, but that's the next blog post. What's a VAR?I try to keep this blog accessible to nonspecialists, so I'll step back momentarily to explain how we produce graphs like these. Economists who know what a VAR is should skip to the next section heading. How do we measure the effect of monetary policy on other variables? Milton Friedman and Anna Schwartz kicked it off in the Monetary History by pointing to the historical correlation of money growth with inflation and output. They knew as we do that correlation is not causation, so they pointed to the fact that money growth preceeded inflation and output growth.  But as James Tobin pointed out, the cock's crow comes before, but does not cause, the sun to rise. So too people may go get out some money ahead of time when they see more future business activity on the horizon.  Even correlation with a lead is not causation. What to do? Clive Granger's causality and Chris Sims' VAR, especially "Macroeconomics and Reality" gave today's answer. (And there is a reason that everybody mentioned so far has a Nobel prize.) First, we find a monetary policy "shock," a movement in the interest rate (these days; money, then) that is plausibly not a response to economic events and especially to expected future economic events. We think of the Fed setting interest rates by a response to economic data plus deviations from that response, such as interest rate = (#) output + (#) inflation + (#) other variables + disturbance. We want to isolate the "disturbance," movements in the interest rate not taken in response to economic events. (I use "shock" to mean an unpredictable variable, and "disturbance" to mean deviation from an equation like the above, but one that can persist for a while. A monetary policy "shock" is an unexpected movement in the disturbance.)  The "rule" part here can be but need not be the Taylor rule, and can include other variables than output and inflation. It is what the Fed usually does given other variables, and therefore (hopefully) controls for reverse causality from expected future economic events to interest rates. Now, in any individual episode, output and inflation and inflation following a shock will be influenced by subsequent shocks to the economy, monetary and other. But those average out. So, the average value of inflation, output, employment, etc. following a monetary policy shock is a measure of how the shock affects the economy all on its own. That is what has been plotted above. VARs were one of the first big advances in the modern empirical quest to find "exogenous" variation and (somewhat) credibly find causal relationships. Mostly the huge literature varies on how one finds the "shocks." Traditional VARs use regressions of the above equations and the residual is the shock, with a big question just how many and which contemporaneous variables one adds in the regression. Romer and Romer pioneered the "narrative approach," reading the Fed minutes to isolate shocks.  Some technical details at the bottom and much more discussion below. The key is finding shocks. One can just regress output and inflation on the shocks to produce the response function, which is a "local projection" not a "VAR," but I'll use "VAR" for both techniques for lack of a better encompassing word. Losing faithShocks, what shocks?What's a "shock" anyway? The concept is that the Fed considers its forecast of inflation, output and other variables it is trying to control, gauges the usual and appropriate response, and then adds 25 or 50 basis points, at random, just for the heck of it. The question VARS try to answer is the same: What happens to the economy if the Fed raises interest rates unexpectedly, for no particular reason at all? But the Fed never does this. Ask them. Read the minutes. The Fed does not roll dice. They always raise or lower interest rates for a reason, that reason is always a response to something going on in the economy, and most of the time how it affects forecasts of inflation and employment. There are no shocks as defined.I speculated here that we might get around this problem: If we knew the Fed was responding to something that had no correlation with future output, then even though that is an endogenous response, then it is a valid movement for estimating the effect of interest rates on output. My example was, what if the Fed "responds" to the weather. Well, though endogenous, it's still valid for estimating the effect on output. The Fed does respond to lots of things, including foreign exchange, financial stability issues, equity, terrorist attacks, and so forth. But I can't think of any of these in which the Fed is not thinking of these events for their effect on output and inflation, which is why I never took the idea far. Maybe you can. Shock isolation also depends on complete controls for the Fed's information. If the Fed uses any information about future output and inflation that is not captured in our regression, then information about future output and inflation remains in the "shock" series. The famous "price puzzle" is a good example. For the first few decades of VARs, interest rate shocks seemed to lead to higher inflation. It took a long specification search to get rid of this undesired result. The story was, that the Fed saw inflation coming in ways not completely controlled for by the regression. The Fed raised interest rates to try to forestall the inflation, but was a bit hesitant about it so did not cure the inflation that was coming. We see higher interest rates followed by higher inflation, though the true causal effect of interest rates goes the other way. This problem was "cured" by adding commodity prices to the interest rate rule, on the idea that fast-moving commodity prices would capture the information the Fed was using to forecast inflation. (Interestingly these days we seem to see core inflation as the best forecaster, and throw out commodity prices!) With those and some careful orthogonalization choices, the "price puzzle" was tamped down to the one year or so delay you see above. (Neo-Fisherians might object that maybe the price puzzle was trying to tell us something all these years!) Nakamura and Steinsson write of this problem: "What is being assumed is that controlling for a few lags of a few variables captures all endogenous variation in policy... This seems highly unlikely to be true in practice. The Fed bases its policy decisions on a huge amount of data. Different considerations (in some cases highly idiosyncratic) affect policy at different times. These include stress in the banking system, sharp changes in commodity prices, a recent stock market crash, a financial crisis in emerging markets, terrorist attacks, temporary investment tax credits, and the Y2K computer glitch. The list goes on and on. Each of these considerations may only affect policy in a meaningful way on a small number of dates, and the number of such influences is so large that it is not feasible to include them all in a regression. But leaving any one of them out will result in a monetary policy "shock" that the researcher views as exogenous but is in fact endogenous." Nakamura and Steinsson offer 9/11 as another example summarizing my "high frequency identification" paper with Monika Piazzesi: The Fed lowered interest rates after the terrorist attack, likely reacting to its consequences for output and inflation. But VARs register the event as an exogenous shock.Romer and Romer suggested that we use Fed Greenbook forecasts of inflation and output as controls, as those should represent the Fed's complete information set. They provide narrative evidence that Fed members trust Greenback forecasts more than you might suspect. This issue is a general Achilles heel of empirical macro and finance: Does your procedure assume agents see no more information than you have included in the model or estimate? If yes, you have a problem. Similarly, "Granger causality" answers the cock's crow-sunrise problem by saying that if unexpected x leads unexpected y then x causes y. But it's only real causality if the "expected" includes all information, as the price puzzle counterexample shows. Just what properties do we need of a shock in order to measure the response to the question, "what if the Fed raised rates for no reason?" This strikes me as a bit of an unsolved question -- or rather, one that everyone thinks is so obvious that we don't really look at it. My suggestion that the shock only need be orthogonal to the variable whose response we're estimating is informal, and I don't know of formal literature that's picked it up. Must "shocks" be unexpected, i.e. not forecastable from anything in the previous time information set? Must they surprise people? I don't think so -- it is neither necessary nor sufficient for  shock to be unforecastable for it to identify the inflation and output responses. Not responding to expected values of the variable whose response you want to measure should be enough.  If bond markets found out about a random funds rate rise one day ahead, it would then be an "expected" shock, but clearly just as good for macro. Romer and Romer have been criticized that their shocks are predictable, but this may not matter. The above Nakamura and Steinsson quote says leaving out any information leads to a shock that is not strictly exogenous. But strictly exogenous may not be necessary for estimating, say, the effect of interest rates on inflation. It is enough to rule out reverse causality and third effects. Either I'm missing a well known econometric literature, as is everyone else writing the VARs I've read who don't cite it, or there is a good theory paper to be written.Romer and Romer, thinking deeply about how to read "shocks" from the Fed minutes, define shocks thus to circumvent the "there are no shocks" problem:we look for times when monetary policymakers felt the economy was roughly at potential (or normal) output, but decided that the prevailing rate of inflation was too high. Policymakers then chose to cut money growth and raise interest rates, realizing that there would be (or at least could be) substantial negative consequences for aggregate output and unemployment. These criteria are designed to pick out times when policymakers essentially changed their tastes about the acceptable level of inflation. They weren't just responding to anticipated movements in the real economy and inflation. [My emphasis.]  You can see the issue. This is not an "exogenous" movement in the funds rate. It is a response to inflation, and to expected inflation, with a clear eye on expected output as well. It really is a nonlinear rule, ignore inflation for a while until it gets really bad then finally get serious about it. Or, as they say, it is a change in rule, an increase in the sensitivity of the short run interest rate response to inflation, taken in response to inflation seeming to get out of control in a longer run sense. Does this identify the response to an "exogenous" interest rate increase? Not really.  But maybe it doesn't matter. Are we even asking an interesting question? The whole question, what would happen if the Fed raised interest rates for no reason, is arguably besides the point. At a minimum, we should be clearer about what question we are asking, and whether the policies we analyze are implementations of that question. The question presumes a stable "rule," (e.g. \(i_t = \rho i_{t-1} + \phi_\pi \pi_t + \phi_x x_t + u_t\)) and asks what happens in response to a deviation \( +u_t \) from the rule. Is that an interesting question? The standard story for 1980-1982 is exactly not such an event. Inflation was not conquered by a big "shock," a big deviation from 1970s practice, while keeping that practice intact. Inflation was conquered (so the story goes) by a change in the rule, by a big increase in $\phi_\pi$. That change raised interest rates,  but arguably without any deviation from the new rule \(u_t\) at all. Thinking in terms of the Phillips curve \( \pi_t = E_t \pi_{t+1} + \kappa x_t\), it was not a big negative \(x_t\) that brought down inflation, but the credibility of the new rule that brought down \(E_t \pi_{t+1}\). If the art of reducing inflation is to convince people that a new regime has arrived, then the response to any monetary policy "shock" orthogonal to a stable "rule" completely misses that policy. Romer and Romer are almost talking about a rule-change event. For 2022, they might be looking at the Fed's abandonment of flexible average inflation targeting and its return to a Taylor rule. However, they don't recognize the importance of the distinction, treating changes in rule as equivalent to a residual. Changing the rule changes expectations in quite different ways from a residual of a stable rule. Changes with a bigger commitment should have bigger effects, and one should standardize somehow by the size and permanence of the rule change, not necessarily the size of the interest rate rise. And, having asked "what if the Fed changes rule to be more serious about inflation," we really cannot use the analysis to estimate what happens if the Fed shocks interest rates and does not change the rule. It takes some mighty invariance result from an economic theory that a change in rule has the same effect as a shock to a given rule. There is no right and wrong, really. We just need to be more careful about what question the empirical procedure asks, if we want to ask that question, and if our policy analysis actually asks the same question. Estimating rules, Clarida Galí and Gertler. Clarida, Galí, and Gertler (2000) is a justly famous paper, and in this context for doing something totally different to evaluate monetary policy. They estimate rules, fancy versions of \(i_t = \rho i_{t-1} +\phi_\pi \pi_t + \phi_x x_t + u_t\), and they estimate how the \(\phi\) parameters change over time. They attribute the end of 1970s inflation to a change in the rule, a rise in \(\phi_\pi\) from the 1970s to the 1980s. In their model, a higher \( \phi_\pi\) results in less volatile inflation. They do not estimate any response functions. The rest of us were watching the wrong thing all along. Responses to shocks weren't the interesting quantity. Changes in the rule were the interesting quantity. Yes, I criticized the paper, but for issues that are irrelevant here. (In the new Keynesian model, the parameter that reduces inflation isn't the one they estimate.) The important point here is that they are doing something completely different, and offer us a roadmap for how else we might evaluate monetary policy if not by impulse-response functions to monetary policy shocks. Fiscal theoryThe interesting question for fiscal theory is, "What is the effect of an interest rate rise not accompanied by a change in fiscal policy?" What can the Fed do by itself? By contrast, standard models (both new and old Keynesian) include concurrent fiscal policy changes when interest rates rise. Governments tighten in present value terms, at least to pay higher interest costs on the debt and the windfall to bondholders that flows from unexpected disinflation. Experience and estimates surely include fiscal changes along with monetary tightening. Both fiscal and monetary authorities react to inflation with policy actions and reforms. Growth-oriented microeconomic reforms with fiscal consequences often follow as well -- rampant inflation may have had something to do with Carter era trucking, airline, and telecommunications reform. Yet no current estimate tries to look for a monetary shock orthogonal to fiscal policy change. The estimates we have are at best the effects of monetary policy together with whatever induced or coincident fiscal and microeconomic policy tends to happen at the same time as central banks get serious about fighting inflation. Identifying the component of a monetary policy shock orthogonal to fiscal policy, and measuring its effects is  a first order question for fiscal theory of monetary policy. That's why I wrote this blog post. I set out to do it, and then started to confront how VARs are already falling apart in our hands. Just what "no change in fiscal policy" means is an important question that varies by application. (Lots more in "fiscal roots" here, fiscal theory of monetary policy here and in FTPL.) For simple calculations, I just ask what happens if interest rates change with no change in primary surplus. One might also define "no change" as no change in tax rates, automatic stabilizers, or even habitual discretionary stimulus and bailout, no disturbance \(u_t\) in a fiscal rule \(s_t = a + \theta_\pi \pi_t + \theta_x x_t + ... + u_t\). There is no right and wrong here either, there is just making sure you ask an interesting question. Long and variable lags, and persistent interest rate movementsThe first plot shows a mighty long lag between the monitor policy shock and its effect on inflation and output. That does not mean that the economy has long and variable lags. This plot is actually not representative, because in the black lines the interest rate itself quickly reverts to zero. It is common to find a more protracted interest rate response to the shock, as shown in the red and blue lines. That mirrors common sense: When the Fed starts tightening, it sets off a year or so of stair-step further increases, and then a plateau, before similar stair-step reversion. That raises the question, does the long-delayed response of output and inflation represent a delayed response to the initial monetary policy shock, or does it represent a nearly instantaneous response to the higher subsequent interest rates that the shock sets off? Another way of putting the question, is the response of inflation and output invariant to changes in the response of the funds rate itself? Do persistent and transitory funds rate changes have the same responses? If you think of the inflation and output responses as economic responses to the initial shock only, then it does not matter if interest rates revert immediately to zero, or go on a 10 year binge following the initial shock. That seems like a pretty strong assumption. If you think that a more persistent interest rate response would lead to a larger or more persistent output and inflation response, then you think some of what we see in the VARs is a quick structural response to the later higher interest rates, when they come. Back in 1988, I posed this question in "what do the VARs mean?" and showed you can read it either way. The persistent output and inflation response can represent either long economic lags to the initial shock, or much less laggy responses to interest rates when they come. I showed how to deconvolute the response function to the structural effect of interest rates on inflation and output and how persistently interest rates rise.  The inflation and output responses might be the same with shorter funds rate responses, or they might be much different. Obviously (though often forgotten), whether the inflation and output responses are invariant to changes in the funds rate response needs a model. If in the economic model only unexpected interest rate movements affect output and inflation, though with lags, then the responses are as conventionally read structural responses and invariant to the interest rate path. There is no such economic model. Lucas (1972) says only unexpected money affects output, but with no lags, and expected money affects inflation. New Keynesian models have very different responses to permanent vs. transitory interest rate shocks. Interestingly, Romer and Romer do not see it this way, and regard their responses as structural long and variable lags, invariant to the interest rate response. They opine that given their reading of a positive shock in 2022, a long and variable lag to inflation reduction is baked in, no matter what the Fed does next. They argue that the Fed should stop raising interest rates. (In fairness, it doesn't look like they thought about the issue much, so this is an implicit rather than explicit assumption.) The alternative view is that effects of a shock on inflation are really effects of the subsequent rate rises on inflation, that the impulse response function to inflation is not invariant to the funds rate response, so stopping the standard tightening cycle would undo the inflation response. Argue either way, but at least recognize the important assumption behind the conclusions. Was the success of inflation reduction in the early 1980s just a long delayed response to the first few shocks? Or was the early 1980s the result of persistent large real interest rates following the initial shock? (Or, something else entirely, a coordinated fiscal-monetary reform... But I'm staying away from that and just discussing conventional narratives, not necessarily the right answer.) If the latter, which is the conventional narrative, then you think it does matter if the funds rate shock is followed by more funds rate rises (or positive deviations from a rule), that the output and inflation response functions do not directly measure long lags from the initial shock. De-convoluting the structural funds rate to inflation response and the persistent funds rate response, you would estimate much shorter structural lags. Nakamura and Steinsson are of this view: While the Volcker episode is consistent with a large amount of monetary nonneutrality, it seems less consistent with the commonly held view that monetary policy affects output with "long and variable lags." To the contrary, what makes the Volcker episode potentially compelling is that output fell and rose largely in sync with the actions [interest rates, not shocks] of the Fed. And that's a good thing too. We've done a lot of dynamic economics since Friedman's 1968 address. There is really nothing in dynamic economic theory that produces a structural long-delayed response to shocks, without the continued pressure of high interest rates. (A correspondent objects to "largely in sync" pointing out several clear months long lags between policy actions and results in 1980. It's here for the methodological point, not the historical one.) However, if the output and inflation responses are not invariant to the interest rate response, then the VAR directly measures an incredibly narrow experiment: What happens in response to a surprise interest rate rise, followed by the plotted path of interest rates? And that plotted path is usually pretty temporary, as in the above graph. What would happen if the Fed raised rates and kept them up, a la 1980? The VAR is silent on that question. You need to calibrate some model to the responses we have to infer that answer. VARs and shock responses are often misread as generic theory-free estimates of "the effects of monetary policy." They are not. At best, they tell you the effect of one specific experiment: A random increase in funds rate, on top of a stable rule, followed by the usual following path of funds rate. Any other implication requires a model, explicit or implicit. More specifically, without that clearly false invariance assumption, VARs cannot directly answer a host of important questions. Two on my mind: 1) What happens if the Fed raises interest rates permanently? Does inflation eventually rise? Does it rise in the short run? This is the "Fisherian" and "neo-Fisherian" questions, and the answer "yes" pops unexpectedly out of the standard new-Keynesian model. 2) Is the short-run negative response of inflation to interest rates stronger for more persistent rate rises? The long-term debt fiscal theory mechanism for a short-term inflation decline is tied to the persistence of the shock and the maturity structure of the debt. The responses to short-lived interest rate movements (top left panel) are silent on these questions. Directly is an important qualifier. It is not impossible to answer these questions, but you have to work harder to identify persistent interest rate shocks. For example, Martín Uribe identifies permanent vs. transitory interest rate shocks, and finds a positive response of inflation to permanent interest rate rises. How? You can't just pick out the interest rate rises that turned out to be permanent. You have to find shocks or components of the shock that are ex-ante predictably going to be permanent, based on other forecasting variables and the correlation of the shock with other shocks. For example, a short-term rate shock that also moves long-term rates might be more permanent than one which does not do so. (That requires the expectations hypothesis, which doesn't work, and long term interest rates move too much anyway in response to transitory funds rate shocks. So, this is not directly a suggestion, just an example of the kind of thing one must do. Uribe's model is more complex than I can summarize in a blog.)  Given how small and ephemeral the shocks are already, subdividing them into those that are expected to have permanent vs. transitory effects on the federal funds rate is obviously a challenge. But it's not impossible. Monetary policy shocks account for small fractions of inflation, output and funds rate variation. Friedman thought that most recessions and inflations were due to monetary mistakes. The VARs pretty uniformly deny that result. The effects of monetary policy shocks on output and inflation add up to less than 10 percent of the variation of output and inflation. In part the shocks are small, and in part the responses to the shocks are small. Most recessions come from other shocks, not monetary mistakes.  Worse, both in data and in models, most inflation variation comes from inflation shocks, most output variation comes from output shocks, etc. The cross-effects of one variable on another are small. And "inflation shock" (or "marginal cost shock"),  "output shock" and so forth are just labels for our ignorance -- error terms in regressions, unforecasted movements -- not independently measured quantities. (This and old point, for example in my 1994 paper with the great title "Shocks." Technically, the variance of output is the sum of the squares of the impulse-response functions -- the plots -- times the variance of the shocks. Thus small shocks and small responses mean not much variance explained.)This is a deep point. The exquisite attention put to the effects of monetary policy in new-Keynesian models, while interesting to the Fed, are then largely beside the point if your question is what causes recessions. Comprehensive models work hard to match all of the responses, not just to monetary policy shocks. But it's not clear that the nominal rigidities that are important for the effects of monetary policy are deeply important to other (supply) shocks, and vice versa. This is not a criticism. Economics always works better if we can use small models that focus on one thing -- growth, recessions, distorting effect of taxes, effect of monetary policy -- without having to have a model of everything in which all effects interact. But, be clear we no longer have a model of everything.  "Explaining recessions" and "understanding the effects of monetary policy" are somewhat separate questions. Monetary policy shocks also account for small fractions of the movement in the federal funds rate itself. Most of the funds rate movement is in the rule, the reaction to the economy term. Like much empirical economics, the quest for causal identification leads us to look at a tiny causes with tiny effects, that do little to explain much variation in the variable of interest (inflation). Well, cause is cause, and the needle is the sharpest item in the haystack. But one worries about the robustness of such tiny effects, and to what extent they summarize historical experience.  To be concrete, here is a typical shock regression, 1960:1-2023:6 monthly data, standard errors in parentheses: ff(t) = a + b ff(t-1) + c[ff(t-1)-ff(t-2)] + d CPI(t) + e unemployment(t) + monetary policy shock, Where "CPI" is the percent change in the CPI (CPIAUCSL) from a year earlier. ff(t-1)ff(t-1)-ff(t-2)CPIUnempR20.970.390.032-0.0170.985(0.009)(0.07)(0.013)(0.009)The funds rate is persistent -- the lag term (0.97) is large. Recent changes matter too: Once the Fed starts a tightening cycle, it's likely to keep raising rates. And the Fed responds to CPI and unemployment. The plot shows the actual federal funds rate (blue), the model or predicted federal funds rate (red), the shock which is the difference between the two (orange) and the Romer and Romer dates (vertical lines). You can't see the difference between actual and predicted funds rate, which is the point. They are very similar and the shocks are small. They are closer horizontally than vertically, so the vertical difference plotted as shock is still visible. The shocks are much smaller than the funds rate, and smaller than the rise and fall in the funds rate in a typical tightening or loosening cycle. The shocks are bunched, with by far the biggest ones in the early 1980s. The shocks have been tiny since the 1980s. (Romer and Romer don't find any shocks!) Now, our estimates of the effect of monetary policy look at the average values of inflation, output, and employment in the 4-5 years after a shock. Really, you say, looking at the graph? That's going to be dominated by the experience of the early 1980s. And with so many positive and negative shocks close together, the average value 4 years later is going to be driven by subtle timing of when the positive or negative shocks line up with later events. Put another way, here is a plot of inflation 30 months after a shock regressed on the shock. Shock on the x axis, subsequent inflation on the y axis. The slope of the line  is our estimate of the effect of the shock on inflation 30 months out (source, with details). Hmm. One more graph (I'm having fun here):This is a plot of inflation for the 4 years after each shock, times that shock. The right hand side is the same graph with an expanded y scale. The average of these histories is our impulse response function.  (The big lines are the episodes which multiply the big shocks of the early 1980s. They mostly converge because, either multiplied by positive or negative shocks, inflation wend down in the 1980s.) Impulse response functions are just quantitative summaries of the lessons of history. You may be  underwhelmed that history is sending a clear story.  Again, welcome to causal economics -- tiny average responses to tiny but identified movements is what we estimate, not broad lessons of history. We do not estimate "what is the effect of the sustained high real interest rates of the early 1980s," for example, or "what accounts for the sharp decline of inflation in the early 1980s?" Perhaps we should, though confronting endogeneity of the interest rate responses some other way. That's my main point today. Estimates disappear after 1982Ramey's first variation in the first plot is to use data from 1983 to 2007. Her second variation is to also omit the monetary variables. Christiano Eichenbaum and Evans were still thinking in terms of money supply control, but our Fed does not control money supply. The evidence that higher interest rates lower inflation disappears after 1983, with or without money. This too is a common finding. It might be because there simply aren't any monetary policy shocks. Still, we're driving a car with a yellowed AAA road map dated 1982 on it.  Monetary policy shocks still seem to affect output and employment, just not inflation. That poses a deeper problem. If there just aren't any monetary policy shocks, we would just get big standard errors on everything. That only inflation disappears points to the vanishing Phillips curve, which will be the weak point in the theory to come. It is the Phillips curve by which lower output and employment push down inflation. But without the Phillips curve, the whole standard story for interest rates to affect inflation goes away. Computing long-run responsesThe long lags of the above plot are already pretty long horizons, with interesting economics still going on at 48 months. As we get interested in long run neutrality, identification via long run sign restrictions (monetary policy should not permanently affect output), and the effect of persistent interest rate shocks, we are interested in even longer run responses. The "long run risks" literature in asset pricing is similarly crucially interested in long run properties. Intuitively, we should know this will be troublesome. There aren't all that many nonoverlapping 4 year periods after interest rate shocks to measure effects, let alone 10 year periods.VARs estimate long run responses with a parametric structure. Organize the data (output, inflation, interest rate, etc) into a vector \(x_t = [y_t \; \pi_t \; i_t \; ...]'\), then the VAR can be written \(x_{t+1} = Ax_t + u_t\). We start from zero, move \(x_1 = u_1\) in an interesting way, and then the response function just simulates forward, with \(x_j = A^j x_1\).  But here an oft-forgotten lesson of 1980s econometrics pops up: It is dangerous to estimate long-run dynamics by fitting a short run model and then finding its long-run implications. Raising matrices to the 48th power \(A^{48}\) can do weird things, the 120th power (10 years) weirder things. OLS and maximum likelihood prize one step ahead \(R^2\), and will happily accept small one step ahead mis specifications that add up to big misspecification 10 years out. (I learned this lesson in the "Random walk in GNP.") Long run implications are driven by the maximum eigenvalue of the \(A\) transition matrix, and its associated eigenvector. \(A^j = Q \Lambda^j Q^{-1}\).  This is a benefit and a danger. Specify and estimate the dynamics of the combination of variables with the largest eigenvector  right, and lots of details can be wrong. But standard estimates aren't trying hard to get these right. The "local projection" alternative directly estimates long run responses: Run regressions of inflation in 10 years on the shock today. You can see the tradeoff: there aren't many non-overlapping 10 year intervals, so this will be imprecisely estimated. The VAR makes a strong parametric assumption about long-run dynamics. When it's right, you get better estimates. When it's wrong, you get misspecification. My experience running lots of VARs is that monthly VARs raised to large powers often give unreliable responses. Run at least a one-year VAR before you start looking at long run responses. Cointegrating vectors are the most reliable variables to include. They are typically the state variable that most reliably carries long - run responses. But pay attention to getting them right. Imposing integrating and cointegrating structure by just looking at units is a good idea. The regression of long-run returns on dividend yields is a good example. The dividend yield is a cointegrating vector, and is the slow-moving state variable. A one period VAR \[\left[ \begin{array}{c} r_{t+1} \\ dp_{t+1} \end{array} \right] = \left[ \begin{array}{cc} 0 & b_r  \\ 0 & \rho \end{array}\right]    \left[ \begin{array}{c} r_{t} \\ dp_{t} \end{array}\right]+ \varepsilon_{t+1}\] implies a long horizon regression \(r_{t+j} = b_r \rho^j dp_{t} +\) error. Direct regressions ("local projections") \(r_{t+j} = b_{r,j} dp_t + \) error give about the same answers, though the downward bias in \(\rho\) estimates is a bit of an issue, but with much larger standard errors. The constraint \(b_{r,j} = b_r \rho^j\) isn't bad. But it can easily go wrong. If you don't impose that dividends and price are cointegrated, or with vector other than 1 -1, if you  allow a small sample to estimate \(\rho>1\), if you don't put in dividend yields at all and just a lot of short-run forecasters, it can all go badly. Forecasting bond returns was for me a good counterexample. A VAR forecasting one-year bond returns  from today's yields gives very different results from taking a monthly VAR, even with several lags, and using \(A^{12}\) to infer the one-year return forecast. Small pricing errors or microstructure dominate the monthly data, which produces junk when raised to the twelfth power. (Climate regressions are having fun with the same issue. Small estimated effects of temperature on growth, raised to the 100th power, can produce nicely calamitous results. But use basic theory to think about units.)  Nakamura and Steinsson (appendix) show how sensitive some standard estimates of impulse response functions are to these questions. Weak evidenceFor the current policy question, I hope you get a sense of how weak the evidence is for the "standard view" that higher interest rates reliably lower inflation, though with a long and variable lag, and the Fed has a good deal of control over inflation. Yes, many estimates look the same, but there is a pretty strong prior going in to that. Most people don't publish papers that don't conform to something like the standard view. Look how long it took from Sims (1980) to Christiano Eichenbaum and Evans (1999) to produce a response function that does conform to the standard view, what Friedman told us to expect in (1968). That took a lot of playing with different orthogonalization, variable inclusion, and other specification assumptions. This is not criticism: when you have a strong prior, it makes sense to see if the data can be squeezed in to the prior. Once authors like Ramey and Nakamura and Steinsson started to look with a critical eye, it became clearer just how weak the evidence is. Standard errors are also wide, but the variability in results due to changes in sample and specification are much larger than formal standard errors. That's why I don't stress that statistical aspect. You play with 100 models, try one variable after another to tamp down the price puzzle, and then compute standard errors as if the 100th model were written in stone. This post is already too long, but showing how results change with different specifications would have been a good addition. For example, here are a few more Ramey plots of inflation responses, replicating various previous estimatesTake your pick. What should we do instead? Well, how else should we measure the effects of monetary policy? One natural approach turns to the analysis of historical episodes and changes in regime, with specific models in mind. Romer and Romer pass on thoughts on this approach: ...some macroeconomic behavior may be fundamentally episodic in nature. Financial crises, recessions, disinflations, are all events that seem to play out in an identifiable pattern. There may be long periods where things are basically fine, that are then interrupted by short periods when they are not. If this is true, the best way to understand them may be to focus on episodes—not a cross-section proxy or a tiny sub-period. In addition, it is valuable to know when the episodes were and what happened during them. And, the identification and understanding of episodes may require using sources other than conventional data.A lot of my and others'  fiscal theory writing has taken a similar view. The long quiet zero bound is a test of theories: old-Keynesian models predict a delation spiral, new-Keynesian models predicts sunspot volatility, fiscal theory is consistent with stable quiet inflation.  The emergence of inflation in 2021 and its easing despite interest rates below inflation likewise validates fiscal vs. standard theories.  The fiscal implications of abandoning the gold standard in 1933 plus Roosevelt's "emergency" budget make sense of that episode. The new-Keynesian reaction parameter \(\phi_\pi\) in \(i_t - \phi_\pi \pi_t\), which leads to unstable dynamics for ](\phi_\pi>1\) is not identified by time series data. So use "other sources," like plain statements on the Fed website about how they react to inflation. I already cited Clarida Galí and Gertler, for measuring the rule not the response to the shock, and explaining the implications of that rule for their model. Nakamura and Steinsson likewise summarize Mussa's (1986) classic study of what happens when countries switch from fixed to floating exchange rates: "The switch from a fixed to a flexible exchange rate is a purely monetary action. In a world where monetary policy has no real effects, such a policy change would not affect real variables like the real exchange rate. Figure 3 demonstrates dramatically that the world we live in is not such a world."Also, analysis of particular historical episodes is enlightening. But each episode has other things going on and so invites alternative explanations. 90 years later, we're still fighting about what caused the Great Depression. 1980 is the poster child for monetary disinflation, yet as Nakamura and Steinsson write, Many economists find the narrative account above and the accompanying evidence about output to be compelling evidence of large monetary nonneutrality. However, there are other possible explanations for these movements in output. There were oil shocks both in September 1979 and in February 1981.... Credit controls were instituted between March and July of 1980. Anticipation effects associated with the phased-in tax cuts of the Reagan administration may also have played a role in the 1981–1982 recession ....Studying changes in regime, such as fixed to floating or the zero bound era, help somewhat relative to studying a particular episode, in that they have some of the averaging of other shocks. But the attraction of VARs will remain. None of these produces what VARs seemed to produce, a theory-free qualitative estimate of the effects of monetary policy. Many tell you that prices are sticky, but not how prices are sticky. Are they old-Keynesian backward looking sticky or new-Keynesian rational expectations sticky? What is the dynamic response of relative inflation to a change in a pegged exchange rate? What is the dynamic response of real relative prices to productivity shocks? Observations such as Mussa's graph can help to calibrate models, but does not answer those questions directly.  My observations about the zero bound or the recent inflation similarly seem (to me) decisive about one class of model vs. another, at least subject to Occam's razor about epicycles, but likewise do not provide a theory-free  impulse response function.  Nakamura and Steinsson write at length about other approaches; model-based moment matching and use of micro data in particular. This post is going on too long; read their paper. Of course, as we have seen, VARs only seem to offer a model-free quantitative measurement of "the effects of monetary policy," but it's hard to give up on the appearance of such an answer. VARs and impulse responses also  remain very useful ways of summarizing the correlations and cross correlations of data, even without cause and effect interpretation. In the end, many ideas are successful in economics when they tell researchers what to do, when they offer a relatively clear recipe for writing papers. "Look at episodes and think hard is not such  recipe." "Run a VAR is." So, as you think about how we can evaluate monetary policy, think about a better recipe as well as a good answer.  (Stay tuned. This post is likely to be updated a few times!) VAR technical appendixTechnically, running VARs is very easy, at least until you start trying to smooth out responses with Bayesian  and other techniques. Line up the data in a vector, i.e. \(x_t = [i_t \; \pi_t\; y_t]'\). Then run a regression of each variable on lags of the others, \[x_t = Ax_{t-1} + u_t.\] If you want more than one lag of the right hand variables, just make a bigger \(x\) vector,  \(x_t = [i_t\; \pi_t \; y_t \; i_{t-1}\; \pi_{t-1} \;y_{t-1}]'.\) The residuals of such regressions \(u_t\) will be correlated, so you have to decide whether, say, the correlation between interest rate and inflation shocks means the Fed responds in the period to inflation, or inflation responds within the period to interest rates, or some combination of the two. That's the  "identification" assumption issue. You can write it as a matrix \(C\) so that \(u_t = C \varepsilon_t\) and cov\((\varepsilon_t \varepsilon_t')=I\) or you can include some contemporaneous values into the right hand sides. Now, with \(x_t = Ax_{t-1} + C\varepsilon_t\), you start with \(x_0=0\), choose one series to shock, e.g. \(\varepsilon_{i,1}=1\) leaving the others alone, and just simulate forward. The resulting path of the other variables is the above plot, the "impulse response function." Alternatively you can run a regression \(x_t = \sum_{j=0}^\infty \theta_j \varepsilon_{t-j}\) and the \(\theta_j\) are (different, in sample) estimates of the same thing. That's "local projection". Since the right hand variables are all orthogonal, you can run single or multiple regressions. (See here for equations.) Either way, you have found the moving average representation, \(x_t = \theta(L)\varepsilon_t\), in the first case with \(\theta(L)=(I-AL)^{-1}C\) in the second case directly. Since the right hand variables are all orthogonal, the variance of the series is the sum of its loading on all of the shocks, \(cov(x_t) = \sum_{j=0}^\infty \theta_j \theta_j'\). This "forecast error variance decomposition" is behind my statement that small amounts of inflation variance are due to monetary policy shocks rather than shocks to other variables, and mostly inflation shocks.   Update:Luis Garicano has a great tweet thread explaining the ideas with a medical analogy. Kamil Kovar has a nice follow up blog post, with emphasis on Europe. He makes a good point that I should have thought of: A monetary policy "shock" is a deviation from a "rule." So, the Fed's and ECB's failure to respond to inflation as they "usually" do in 2021-2022 counts exactly the same as a 3-5% deliberate lowering of the interest rate. Lowering interest rates for no reason, and leaving interest rates alone when the regression rule says raise rates are the same in this methodology.  That "loosening" of policy was quickly followed by inflation easing, so an updated VAR should exhibit a strong "price puzzle" -- a negative shock is followed by less, not more inflation. Of course historians and practical people might object that failure to act as usual has exactly the same effects as acting. * Some Papers: Comment on Romer and Romer What ends recessions? Some "what's a shock?"Comment on Romer and Romer A new measure of monetary policy. The greenbook forecasts, and beginning thoughts that strict exogeneity is not necessary. Shocks monetary shocks explain small fractions of output variance.Comments on Hamilton, more thoughts on what a shock is.What do the VARs mean? cited above, is the response to the shock or to persistent interest rates?The Fed and Interest Rates, with Monika Piazzesi. Daily data and interest rates to identify shocks. Decomposing the yield curve with Monika Piazzesi. Starts with a  great example of how small changes in specification lead to big differences in long run forecasts. Time seriesA critique of the application of unit root tests pretesting for unit roots and cointegration is a bad ideaHow big is the random walk in GNP? lessons in not using short run dynamics to infer long run properties. Permanent and transitory components of GNP and stock prices a favorite of cointegration really helps on long run propertiesTime series for macroeconomics and finance notes that never quite became a book. Explains VARs and responses.