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This volume of Advances in Econometrics focuses on recent developments in the use of structural econometric models in empirical economics. The papers in this volume are divided in to three broad groups. The first part looks at recent developments in the estimation of dynamic discrete choice models. This includes using new estimation methods for these models based on Euler equations, estimation using sieve approximation of high dimensional state space, the identification of Markov dynamic games with persistent unobserved state variables and developing test of monotone comparative static in models of multiple equilibria. The second part looks at recent advances in the area empirical matching models. The papers in this section look at developing estimators for matching models based on stability conditions, estimating matching surplus functions using generalized entropy functions, solving for the fixed point in the Choo-Siow matching model using a contraction mapping formulation. While the issue of incomplete, or partial identification of model parameters is touched upon in some of the foregoing chapters, two chapters focus on this issue, in the context of testing for monotone comparative statics in models with multiple equilibria, and estimation of supermod- ular games under the restrictions that players' strategies be rationalizable. The last group of three papers looks at empirical applications using structural econometric models. Two application applies matching models to solve en- dogenous matching to the loan spread equation and to endogenize marriage in the collective model of intrahousehold allocation. Another applications looks at market power of condominium developers in the Japanese housing market in the 1990s.

Econometric models are widely used in the creation and evaluation of economic policy in the public and private sectors. But these models are useful only if they adequately account for the phenomena in question, and they can be quite misleading if they do not. In response, econometricians have developed tests and other checks for model adequacy. All of these methods, however, take as given the specification of the model to be tested. In this book, John Geweke addresses the critical earlier stage of model development, the point at which potential models are inherently incomplete. Summarizing and extending recent advances in Bayesian econometrics, Geweke shows how simple modern simulation methods can complement the creative process of model formulation. These methods, which are accessible to economics PhD students as well as to practicing applied econometricians, streamline the processes of model development and specification checking. Complete with illustrations from a wide variety of applications, this is an important contribution to econometrics that will interest economists and PhD students alike.

"Economic Models for Industrial Organization focuses on the specification and estimation of econometric models for research in industrial organization. In recent decades, empirical work in industrial organization has moved towards dynamic and equilibrium models, involving econometric methods which have features distinct from those used in other areas of applied economics. These lecture notes, aimed for a first or second-year PhD course, motivate and explain these econometric methods, starting from simple models and building to models with the complexity observed in typical research papers. The covered topics include discrete-choice demand analysis, models of dynamic behavior and dynamic games, multiple equilibria in entry games and partial identification, and auction models."--Publisher's website

"This book presents Professor Lawrence R Klein and his group's last quarterly econometric model of the United States economy that they had produced at the University of Pennsylvania. This is the last econometric model that Lawrence Klein and his disciples have left after some 50 years of cumulated efforts of constructing the US economy model up to around 2000. It was widely known as the WEFA Econometric Model Mark 10, and is the culmination of Professor Klein's research which spans more than 70 years, and would please not only Professor Klein's old students and colleagues, but also younger students who have heard so much of Klein models but have yet to see the latest model in its complete and printed form."--

This article addresses the lack of cohesion in econometric model building. This incoherence contributes to model building based on statistical criteria—correcting residuals—and not theoretical criteria. The models we build, therefore, are not valid replications of theory. To deal with this problem, an agenda for model building is outlined and discussed. Drawing on the methodological approaches of Hendry, Qin, and Favero (1989), Hendry and Richard (1982, 1983), Sargan (1964), and Spanos (1986), this agenda incorporates a "general to simple" modeling philosophy, a battery of diagnostic tests, reduction theory, and the development of models that include short-term and long-term parameters. A comparison is made between a model based on this agenda and a model based on corrected residuals. The findings show that the agenda-based model outperforms the residual correction model.

The purpose of this thesis is to provide a few new ideas to the field of Bayesian econometrics. In particular, the focus of the thesis is on analyzing dynamic econometric models. In the first essay, we provide an easily implementable method for the Bayesian analysis of a simple hybrid DSGE model of Clarida et al. (1999). The forecasting properties of the model are tested against commonly used forecasting tools, such as Bayesian VARs and naïve forecasts based on univariate random walks. In particular, the predictability of three key macroeconomic-variables, inflation, short-term nominal interest rate and a measure of output gap, are studied using quarterly ex post and real-time U.S. data.Our posterior evidence implies that the simple hybrid model captures the predictable behavior of the three U.S. key macroeconomic variables very well. The result is very interesting, since several recent Bayesian papers have suggested different ways to improve the forecasting performance of DSGE models at the cost of increasing the complexity of model mechanisms, thus decreasing the practicability of these approaches. In the second essay, using GARCH-in-Mean models based on the following Intertemporal Capital Asset Pricing Model Et-1(rt) = μ0 + μ1Vart-1(rt) for the expected excess return rt, we study the robustness of the risk-return relationship in U.S. stock market returns. The issue is important, since unnecessarily including μ0 in the previous expression is known to distort conclusions, while restricting μ0 to zero forces the expected excess return to equal the risk-free interest rate under the hypothesis μ1 = 0. The latter is in conflict with the stock premium puzzle literature. To check the empirical relevance of the theoretical restriction μ0 = 0, we assume a zero mean normal prior distribution for μ0 with several alternative plausible values for its prior variance to see how the tightness of this prior assumption affects the estimation results.Our evidence indicates that the existence of a risk-return relationship is fairly robust in that it does not strongly depend on the prior beliefs concerning the intercept, especially when the true value of μ0 is sufficiently close to zero. In the third essay, we expand Kleibergen and Zivot's (2003) Bayesian Two Stage (B2S) model by allowing for unequal variances. To the best of our knowledge there is no single Bayesian study of instrumental variable (IV) models with unequal variances, although from the Bayesian point of view modelling heteroscedasticity should improve the precision of estimates and the quality of predictive inference. As an application we present a cross-country Cobb-Douglas production function estimation, since the problems of endogeneity and heteroscedasticity are well documented in the cross-country growth literature. In the fourth essay, we provide a simple epidemiology model where households, when forming their inflation expectations, rationally adopt the past release of inflation with certain probability rather than the forward-looking newspaper forecast as suggested in previous literature. The posterior model probabilities based on the Michigan survey data strongly support the proposed model.We also extend the agent-based epidemiology model by deriving for it a simple adaptation, which is suitable for estimation. Our results show that this model is able to capture the heterogeneity in households' expectations very well. In the fifth essay, the Bayesian structural vector autoregressive model and the Finnish aggregate infrastructure capital series from 1860 to 2003 are used to explore how government infrastructure policy affects long-run output growth. The Finnish data is used, since to our knowledge the Finnish land and water construction investments series is the best available sufficiently long time series on aggregate infrastructure investments. We base our conclusions on posterior analysis, since it allows us to draw exact inference on parameters with near non-stationary data. We find strong and robust support in the Finnish data to indicate that permanent changes in government infrastructure policy can have permanent effects on the growth rate of output ; Jani Luoto kartoitti väitöstutkimuksessaan bayesilaisten menetelmien soveltuvuutta taloustieteelliseen tutkimukseen. Väitöskirjassa tarjotaan uusia sovelluksia bayesilaisen ekonometrian kenttään. Väitös esittelee uuden ekonometrisen mallin ja subjektivismin hyödyntämistä talousteoreettisten rajoitteiden validiuden testaamisessa. Lisäksi se tarjoaa uuden tehokkaan tavan parantaa suosittujen uusien keynesiläisten makromallien ennustetarkkuutta.Luoto havaitsi, että bayesilaiset menetelmät tuottavat luotettavia empiirisiä tuloksia useissa sellaisissa tapauksissa, joissa valtavirtaekonometria saattaa epäonnistua.- Bayesilaiset menetelmät vaikuttavat olevan erityisen hyödyllisiä varsinkin silloin, kun vertailtavat taloustieteelliset mallit ovat ei-sisäkkäisiä tai mallin uskottavuusfunktio on monihuippuinen tai se saavuttaa maksimiarvonsa taloudellisessa mielessä järjettömillä parametriarvoilla, Luoto kertoo.Usein taloustieteellinen teoria tarjoaa tärkeää tai välttämätöntä tietoa mallinnettavasta ilmiöstä, jota käsillä oleva aineisto ei pysty tarjoamaan. Tätä tietoa voidaan käyttää hyväksi bayesilaisissa sovelluksissa. Luodon väitöskirja laajentaa siten taloustieteellisellä alalla käytettävissä olevaa tutkimusvälineistöä.Luodon väitöskirja koostuu viidestä esseestä. Esseissä bayesilaisia metodeja käytetään ratkaisemaan ongelmia, joita ei pystytä helposti ratkaisemaan perinteisen tilastotieteen keinoin. Esseet tuovat oman lisäarvonsa taloustieteelliseen tutkimukseen.Ensimmäisessä esseessä esitellään helposti sovellettavissa oleva estimointitapa uuden keynesiläisen dynaamisen stokastisen yleisen tasapainon (DSGE) mallin implementointiin. Esseessä tutkitaan kuinka hyvin kyseinen malli kykenee ennustamaan talouden avainmuuttujia: inflaatiota, korkoa ja tuotantoa. Tutkimustulokset osoittavat, että kyseinen yksinkertainen makromalli suoriutuu tehtävästään hyvin suhteessa vaihtoehtoisiin yleisesti käytettyihin ennustemalleihin.- Tulos on hyvin mielenkiintoinen, koska aikaisemmassa kirjallisuudessa samoihin tuloksiin on päästy vain kasvattamalla voimakkaasti käytettyjen makromallien kokoa ja siten heikentämällä analyysin käyttökelpoisuutta, Luoto toteaa.Toisessa esseessä tutkitaan markkinaportfolion tuoton ja riskin välisen suhteen robustisuutta. Tutkimuksen lähtökohtana on teoreettinen oletus, että markkinoilta ei ole saatavissa ylisuuria tuottoja. Esitetyt tulokset indikoivat, että tuoton ja riskin välinen suhde on mallinnettavissa vain kyseisen teoreettisen priori-rajoitteen alaisuudessa. Toisaalta priori-rajoite on hyväksyttävissä vain ylituottoa mittaavan parametrin todellisen arvon ollessa tarpeeksi lähellä nollaa, mutta muussa tapauksessa rajoite on kyseenalainen.Kolmas essee esittelee uuden ekonometrisen mallin, joka sallii bayesilaisessa instrumenttimuuttujamallissa ei-vakioisen mallivirheen varianssin.- Tutkimus on ensimmäinen laatuaan, mikä on suhteellisen yllättävää, sillä heteroskedastisuuden mallintaminen bayesilaisessa ympäristössä parantaa estimaattien tarkkuutta ja ennustepäättelyn laatua. Käytännön sovelluksena tutkimuksessa estimoidaan Cobb-Douglas tuotantofunktio käyttämällä maakohtaista poikkileikkausaineistoa. Sovellus on relevantti, sillä alan kirjallisuus on huolellisesti raportoinut endogeenisuus- ja heteroskedastisuusongelmien olemassaolosta tämän tyyppisissä aineistoissa, Jani Luoto kertoo.Neljännessä esseessä esitellään uusi kotitalouksien inflaatio-odotuksia mallintava malli. Mallissa luovutaan rationaalisten kotitalouksien oletuksesta, ja oletetaan, että taloudenpitäjät muodostavat oletuksensa mediassa esitettyjen inflaatiouutisten perusteella. Malli olettaa, että taloudenpitäjät havaitsevat inflaatioluvun mediasta tietyllä todennäköisyydellä. Estimoitujen mallien posteriori-todennäköisyyksien perusteella Yhdysvaltain aineisto tukee voimakkaasti väitöskirjassa esitettyä mallia, suhteessa malliin jossa taloudenpitäjät ovat osittain rationaalisia.Viidennessä esseessä tutkitaan infrastruktuuri-investointien pysyviä kasvuvaikutuksia käyttäen hyväksi hyvin pitkän aikavälin (1860–2003) aggregaattitason infrastruktuuri-investointien vuosiaineistoa. Tutkimuksen tilastollisessa päättelyssä käytetään posteriorianalyysiä, koska se tekee eksaktin päättelyn helpoksi myös yksikköjuuriaineistoilla. Tutkimustulokset indikoivat, että myös hyvin pitkällä aikavälillä infrastruktuurishokit aiheuttavat pysyviä vaikutuksia bruttokansantuotteeseen.