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Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1.

We use newly-available Indian panel data to estimate how the returns to planting-stage investments vary by rainfall realizations. We show that the forecasts significantly affect farmer investment decisions and that these responses account for a substantial fraction of the inter-annual variability in planting-stage investments, that the skill of the forecasts varies across areas of India, and that farmers respond more strongly to the forecast where there is more forecast skill and not at all when there is no skill. We show, using an IV strategy in which the Indian government forecast of monsoon rainfall serves as the main instrument, that the return to agricultural investment depends substantially on the conditions under which it is estimated. Using the full rainfall distribution and our profit function estimates, we find that Indian farmers on average under-invest, by a factor of three, when we compare actual levels of investments to the optimal investment level that maximizes expected profits. Farmers who use skilled forecasts have increased average profit levels but also have more variable profits compared with farmers without access to forecasts. Even modest improvements in forecast skill would substantially increase average profits.

This chapter will discuss real-time forecasting in a macroeconomic policy context. I will begin by talking about the Survey of Professional Forecasters (SPF), a survey of private-sector forecasters. Next, I will discuss research on real-time data analysis and its importance in forecasting. Finally, I will discuss real-time forecasting in the 1990s.

The behavior of the individual Spanish voter has come to be rather well-understood, thanks to a growing research literature. However, no models have appeared to explain, or to forecast, national election outcomes. The presence of this research gap contrasts sharply with the extensive election forecasting work done on other leading Western democracies. Here we fill this gap. The model, developed from core political economy theory, is parsimonious but statistically robust. Further, it promises considerable prediction accuracy of Spanish general election outcomes, six months before the contest actually occurs. After presenting the model, and carrying out extensive regression diagnostics, we offer an ex ante forecast of the 2012 general election. ; Fundação para a Ciência e a Tecnologia ...

Wage inequality in Chile has remained high for decades and it is currently at the center of the political agenda. Increasing education of workers is expected to contribute to reduce wage inequality. Based on historical trends of age, education, and returns to education, this paper attempts to forecast wage inequality. Despite an increase in average earnings due to higher levels of education of workers, high levels of wage inequality within age groups and within education groups produce that forecasted wage inequality remains high for the next 10-year period. The structure of the Chilean labor market appears to imply that there is a high level of underlying wage inequality. Nevertheless, the good news are that the labor market structure seems to prevent further deteriorations of wage inequality.

This paper presents an early warning system as a set of multi-period forecasts of indicators of tail real and financial risks obtained using a large database of monthly US data for the period 1972:1-2014:12. Pseudo-real-time forecasts are generated from: (a) sets of autoregressive and factor-augmented vector autoregressions (VARs), and (b) sets of autoregressive and factor-augmented quantile projections. Our key finding is that forecasts obtained with AR and factor-augmented VAR forecasts significantly underestimate tail risks, while quantile projections deliver fairly accurate forecasts and reliable early warning signals for tail real and financial risks up to a 1-year horizon.

To what extent do frequently cited determinants of military spending allow us to predict and forecast future levels of expenditure? The authors draw on the data and specifications of a recent model on military expenditure and assess the predictive power of its variables using in-sample predictions, out-of-sample forecasts and Bayesian model averaging. To this end, this paper provides guidelines for prediction exercises in general using these three techniques. More substantially, however, the findings emphasize that previous levels of military spending as well as a country?s institutional and economic characteristics particularly improve our ability to predict future levels of investment in the military. Variables pertaining to the international security environment also matter, but seem less important. In addition, the results highlight that the updated model, which drops weak predictors, is not only more parsimonious, but also slightly more accurate than the original specification.

Stock market volatility has been an important subject in the finance literature for which now an enormous body of research exists. Volatility modelling and forecasting have been in the epicentre of this line of research and although more than a few models have been proposed and key parameters on improving volatility forecasts have been considered, finance research has still to reach a consensus on this topic. This thesis enters the ongoing debate by carrying out empirical investigations by comparing models from the current pool of models as well as exploring and proposing the use of further key parameters in improving the accuracy of volatility modelling and forecasting. The importance of accurately forecasting volatility is paramount for the functioning of the economy and everyone involved in finance activities. For governments, the banking system, institutional and individual investors, researchers and academics, knowledge, understanding and the ability to forecast and proxy volatility accurately is a determining factor for making sound economic decisions. Four are the main contributions of this thesis. First, the findings of a volatility forecasting model comparison reveal that the GARCH genre of models are superior compared to the more 'simple' models and models preferred by practitioners. Second, with the use of backward recursion forecasts we identify the appropriate in-sample length for producing accurate volatility forecasts, a parameter considered for the first time in the finance literature. Third, further model comparisons are conducted within a Value-at-Risk setting between the RiskMetrics model preferred by practitioners, and the more complex GARCH type models, arriving to the conclusion that GARCH type models are dominant. Finally, two further parameters, the Volatility Index (VIX) and Trading Volume, are considered and their contribution is assessed in the modelling and forecasting process of a selection of GARCH type models. We discover that although accuracy is improved upon, GARCH type forecasts are still superior.

The World Health Organization declared the coronavirus disease 2019 a pandemic on March 11th, pointing to the over 118,000 cases in over 110 countries and territories around the world at that time. At the time of writing this manuscript, the number of confirmed cases has been surging rapidly past the half-million mark, emphasizing the sustained risk of further global spread. Governments around the world are imposing various containment measures while the healthcare system is bracing itself for tsunamis of infected individuals that will seek treatment. It is therefore important to know what to expect in terms of the growth of the number of cases, and to understand what is needed to arrest the very worrying trends. To that effect, we here show forecasts obtained with a simple iteration method that needs only the daily values of confirmed cases as input. The method takes into account expected recoveries and deaths, and it determines maximally allowed daily growth rates that lead away from exponential increase toward stable and declining numbers. Forecasts show that daily growth rates should be kept at least below 5% if we wish to see plateaus any time soon—unfortunately far from reality in most countries to date. We provide an executable as well as the source code for a straightforward application of the method on data from other countries.

Mortality forecasts are essential for predicting the future extent of population ageing, and for determining the sustainability of pension schemes and social security systems. They are also useful in setting life insurance premiums, and in helping governments plan for the changing needs of their societies for health care and other services (European Commission 2009). Nowadays, the societal importance of accurate mortality forecasts is greater than ever before. As a strategy for dealing with rapid population ageing, recent pension reforms in a number of low-mortality countries have made an explicit link between the retirement age and/or retirement payments and past and future anticipated mortality and life expectancy values (Carone et al. 2016; OECD 2016). Because of the large and increasing societal relevance of accurate mortality forecasts, the field of mortality forecasting is growing and advancing.

This dissertation thesis consists of three academic papers. The first paper analyses the macroeconomic effects of labor market reforms in Spain and the spillovers to the rest of the euro area using a two country monetary union DSGE model. The second paper provides a thorough assessment of the macroeconomic effects of fiscal consolidation in a dynamic general equilibrium model for the US economy. The third paper studies the performance of single predictor bridge equation models as well as a wide range of model selection and pooling techniques, including Mallows model averaging and Cross-Validation model averaging, for short-term forecasting of euro area GDP growth.

Summary This work explores the possibilities of forecasting German gas imports and German gas consumption in detail by: • Identifying proper terminology, with initial thoughts on the status quo of forecasting, using analogies with music (section 3.1), • Understanding forecasting models based on statistics and machine learning (section 3.2), • Applying domain knowledge while constructing data sets (data acquisition) - various models have been tested on low resolution data sets (monthly and yearly data). In classical forecasting, forecasts are conducted at company level (e.g. electricity load forecasting) for a short-term period fulfilling the requirement of profit maximization. In contrast, this work's forecasts spatially cover 1) all import nodes into Germany, 2) gas consumption in Germany with the focus on energy security. • Examining various notions of complexity in relation to forecasting Novelty in methodology • Considering infrastructure as the upper limit for the forecasting model (section 4.1) • Testing models for ex post forecasting of gas imports into Germany (section 4.2) • Testing models for ex post forecasting of gas consumption in Germany (section 4.3). Producing an ex ante forecast by answering "what will be the future gas consumption in Germany in next ten years" would cause the forecasting error to increase dramatically due to the uncertainty of future input values, such as population (lower risk) or, weather (higher uncertainty), which would lead to prediction intervals being too wide to make any statement about the future imports or consumption. • Applying complexity measures such as approximate entropy ApEn and sample entropy to two self-constructed real-world data sets on gas imports to Germany and gas consumption in Germany (section 3.3) ; Diese Arbeit untersucht die Möglichkeiten zur Prognose von Deutschen Gasimporten sowie des Gasverbrauchs in Deutschland im Detail, wie folgt: • Identifikation klarer Terminologie, mit ersten Gedanken zum Status Quo der Prognosetechnik (Abschnitt 3.1) • ...

Summary This work explores the possibilities of forecasting German gas imports and German gas consumption in detail by: • Identifying proper terminology, with initial thoughts on the status quo of forecasting, using analogies with music (section 3.1), • Understanding forecasting models based on statistics and machine learning (section 3.2), • Applying domain knowledge while constructing data sets (data acquisition) - various models have been tested on low resolution data sets (monthly and yearly data). In classical forecasting, forecasts are conducted at company level (e.g. electricity load forecasting) for a short-term period fulfilling the requirement of profit maximization. In contrast, this work's forecasts spatially cover 1) all import nodes into Germany, 2) gas consumption in Germany with the focus on energy security. • Examining various notions of complexity in relation to forecasting Novelty in methodology • Considering infrastructure as the upper limit for the forecasting model (section 4.1) • Testing models for ex post forecasting of gas imports into Germany (section 4.2) • Testing models for ex post forecasting of gas consumption in Germany (section 4.3). Producing an ex ante forecast by answering "what will be the future gas consumption in Germany in next ten years" would cause the forecasting error to increase dramatically due to the uncertainty of future input values, such as population (lower risk) or, weather (higher uncertainty), which would lead to prediction intervals being too wide to make any statement about the future imports or consumption. • Applying complexity measures such as approximate entropy ApEn and sample entropy to two self-constructed real-world data sets on gas imports to Germany and gas consumption in Germany (section 3.3) ; Diese Arbeit untersucht die Möglichkeiten zur Prognose von Deutschen Gasimporten sowie des Gasverbrauchs in Deutschland im Detail, wie folgt: • Identifikation klarer Terminologie, mit ersten Gedanken zum Status Quo der Prognosetechnik (Abschnitt 3.1) • Erweiterung des Verständnisses von Prognosemodellen, die aus den Themenbereichen der Statistik und des maschinellen Lernens stammen (Abschnitt 3.2) • Anwendung von Fachwissen bei der Konstruktion von Datensätzen (Data acquisition) - verschiedene Modelle wurden auf niedrig aufgelösten Daten getestet. Bei der klassischen Vorgehensweise werden Prognosen auf Unternehmensebene (z.B. Stromlastprognose) für einen kurzfristigen Zeitraum durchgeführt, der die Anforderung der Gewinnmaximierung erfüllt. Im Gegensatz dazu umfassen in diesem Werk präsentierte Prognosen räumlich 1) alle Importknotenpunkte nach Deutschland und 2) den Gasverbrauch in Deutschland mit dem Fokus auf die Energiesicherheit. • Untersuchung verschiedener Begriffe von Komplexität in Bezug auf Prognosen Neuerungen in der Methodik • Betrachtung der Infrastrukturkapazität als Obergrenze für das Prognosemodell (Abschnitt 4.1) • Testen von Modellen für die Ex-post-Prognose von Gasimporten nach Deutschland (Abschnitt 4.2) • Testen von Modellen für die Ex-post-Prognose des Gasverbrauchs in Deutschland (Abschnitt 4.3). Erstellung von Ex-Ante-Prognosen durch Beantwortung der Frage: "Wie hoch wird der zukünftige Gasverbrauch in Deutschland in den nächsten zehn Jahren sein?" würde dazu führen, dass der Prognosefehler aufgrund der Unsicherheit der zukünftigen Werte von Inputs wie Bevölkerung (geringeres Risiko) oder Wetter (höhere Unsicherheit) dramatisch ansteigt. Dadurch würden die Vorhersageintervalle zu groß, um eine zutreffende Aussage über die zukünftigen Importe oder den Verbrauch zu treffen. • Die Anwendung von Komplexitätsmaßen wie der ungefähren Entropie (ApEn) und der Probenentropie auf zwei selbst konstruierte Realwelt-Datensätze zu Gasimporten nach Deutschland und Gasverbrauch in Deutschland (Abschnitt 3.3)

Tez No: 611160 ; Yüksek Lisans ; Büyüme ve gelişmedeki artışla birlikte enerji sektörü dünyadaki en önemli sektörlerden biri haline geldi, bu nedenle çoğu ülke enerji sektörünü iyileştirmek ve başarılı bir gelecek politikası oluşturmak için yarışa girdi. Bu çalışma, karar vermede yardımcı olmak için istatistiksel teknikleri (Holt-Winters ve Box-Jenkins) kullanarak Türkiye'deki elektrik talebini tahmin etmek için modeller oluşturmayı amaçlamaktadır. İstatistiksel modellere uyması için iki zaman serisini (yıllık ve mevsimlik zaman serileri) kullanıyorum. Bu modellerin çoğu yüksek doğruluk oranına sahiptir, ancak yıllık zaman serilerini tahmin etmek için en iyi model, Ortalama Mutlak Yüzde Hata (MAPE) 'nin% 2.518 olduğu ve mevsimsel zaman serilerini tahmin etmek için en iyi model olan SARIMA (2) olan ARIMA (0,2,1)' dır. 1,2) X (1,1,1) MAPE% 2.131 idi. Bu modellerden elde edilen nihai sonuçlar, Aralık 2021'de talebin mevsimsel zaman serileri için 28307 (GW / s) ve 2027'deki talebin 398313 (GW / s) olduğu yerlerde elektrik talebinde bir artış olduğunu göstermektedir. Bu nedenle, talebi karşılamak için elektrik üretimi önümüzdeki dönemde artırılmalıdır. ; With the increase in the growth and development the energy sector become one of the most important sectors in the world therefore, most countries entered into a race to improve their energy sector and draw up a successful future policy. This study aims to create models for forecasting electricity demand in Turkey by using statistical techniques (Holt-Winters and Box-Jenkins) to assist in decision making. I use the two-time series (annual and seasonal time series) to fit statistical models. Most of these models have high accuracy but the best model for forecasting annual time series is the ARIMA (0,2,1) where Mean Absolute Percentage Error (MAPE) was 2.518% and the best model for forecasting seasonal time series is SARIMA (2,1,2)X(1,1,1) where MAPE was 2.131%. The final results from these models show an increase in electricity demand in future where in Dec-2021 the demand becomes 28307 (GW/h) for seasonal time series and the demand in 2027 becomes 398313 (GW/h). The production of electricity, therefore, must be increased in the next period to satisfy demand.

The origins of forecasting can be traced back to the beginning of human civilization with attempts to predict the weather, although forecasting as a field first appeared in the 1940s and attracted more followers from the early 1950s, when the need for predictions emerged in different fields of endeavor. It expanded considerably in the 1960s and 1970s when benefits were ascertained and computers were employed to perform the tedious calculations required. But initial successes in the fields of economics and business were first moderated and later reversed, with reality checks, first during the 1973/74 energy crisis, afterwards during the prolonged economic stagflation of the late 1970s and early 1980s and further deteriorated during the severe 2007/8 global financial crisis. The initial, optimistic expectations that social sciences will (using powerful computers and sophisticated models) replicate the predictive accuracy of hard ones were repeatedly shattered. This has left diverse fields like economics, management, political and human sciences and even worse medicine with no objective evidence of successful, accurate predictions, casting doubts to their "scientific" vigor. At the same time, weather forecasting achieved success for immediate term predictions improving its accuracy and reliability over time. This paper starts with a historical overview of non-superstition based forecasting as it is practiced in different areas and surveys their predictive accuracy, highlighting their successes, identifying their failures and explaining the reasons involved. Consequently, it argues for a new, pragmatic approach where the emphasis must shift from forecasting to assessing uncertainty, as realistically as possible, evaluating its implications to risk and exploring ways to prepare to face it. It expands Rumsfeld's classification to four quadrants (Known/Knowns, Unknown/Knowns, Known/Unknowns and Unknown/Unknowns) in order to explore the full range of predictions and associated uncertainties and consider the implications and risks involved. Finally, there is a concluding section summarizing the findings and providing some suggestions for future research aimed at turning forecasting into an interdisciplinary field increasing its value and usefulness.