In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

This review article tries to answer four questions: (i) what are the stylized facts about uncertainty over time; (ii) why does uncertainty vary; (iii) do fluctuations in uncertainty matter; and (iv) did higher uncertainty worsen the Great Recession of 2007-2009? On the first question both macro and micro uncertainty appears to rise sharply in recessions. On the second question the types of exogenous shocks like wars, financial panics and oil price jumps that cause recessions appear to directly increase uncertainty, and uncertainty also appears to endogenously rise further during recessions. On the third question, the evidence suggests uncertainty is damaging for short-run investment and hiring, but there is some evidence it may stimulate longer-run innovation. Finally, in terms of the Great Recession, the large jump in uncertainty in 2008 potentially accounted for about one third of the drop in GDP.

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Fluctuations in Uncertainty

Measuring Business Cycles

https://hal.archives-ouvertes.fr/hal-00844811/document

A two-step estimator for large approximate dynamic factor models based on Kalman filtering

Historically, time series forecasts of economic variables have used only a handful of predictor variables, while forecasts based on a large number of predictors have been the province of judgmental forecasts and large structural econometric models. The past decade, however, has seen considerable progress in the development of time series forecasting methods that exploit many predictors, and this chapter surveys these methods. The first group of methods considered is forecast combination (forecast pooling), in which a single forecast is produced from a panel of many forecasts. The second group of methods is based on dynamic factor models, in which the comovements among a large number of economic variables are treated as arising from a small number of unobserved sources, or factors. In a dynamic factor model, estimates of the factors (which become increasingly precise as the number of series increases) can be used to forecast individual economic variables. The third group of methods is Bayesian model averaging, in which the forecasts from very many models, which differ in their constituent variables, are averaged based on the posterior probability assigned to each model. The chapter also discusses empirical Bayes methods, in which the hyperparameters of the priors are estimated. An empirical illustration applies these different methods to the problem of forecasting the growth rate of the U.S. index of industrial production with 130 predictor variables.

Forecasting with Many Predictors

This paper compares the GDP forecasting performance of alternative factor models based on monthly time series for the French economy. These models are based on static and dynamic principal components. The dynamic principal components are obtained using time and frequency domain methods. The forecasting accuracy is evaluated in two ways for GDP growth. First, we question whether it is more appropriate to use aggregate or disaggregate data (with three disaggregating levels) to extract the factors. Second, we focus on the determination of the number of factors obtained either from various criteria or from a fixed choice.

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Are Disaggregate Data Useful for Factor Analysis in Forecasting French GDP

The recent burst of the house price bubble in the United States and its spillover effects on real economies worldwide has rekindled the interest in the role of housing in the business cycle. In this paper, we investigate the relationships between housing cycles among the four major euro area countries (Germany, France, Italy and Spain) over the sample 1980Q1-2008Q4. Our main findings are that GDP cycles show a high degree of comovement across these four countries, reflecting trade linkages, but much weaker ones for housing market cycles, where idiosyncratic factors play a major role. House prices are even less related than quantities across countries. We also find much stronger relationships in the common monetary policy period.

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Housing Cycles in the Major Euro Area Countries

This paper deals with the k-factor extension of the long memory Gegenbauer process proposed by Gray et al. (1989). We give the analytic expression of the prediction function derived from this long memory process and provide the h-step-ahead prediction error when parameters are either known or estimated. We investigate the predictive ability of the k-factor Gegenbauer model on real data of urban transport traffic in the Paris area, in comparison with other short- and long-memory models. Copyright © 2001 John Wiley & Sons, Ltd.

Forecasting with k‐factor Gegenbauer Processes: Theory and Applications

In the paper we aim to introduce a statistical dating and detection of turning points giving them a first economic interpretation The main advantage of the proposed approach is represented by the fact that classical and growth cycles are jointly considered both in the dating and in the detecting stage A key result of this choice is a better description of different economic phases as well as a more accurate investigation of the economic cyclical behaviour The proposed approach considerably improves the relevance of information delivered to users in comparison with a standard analysis based only on classical or growth cycle components

/pdf/a-system-for-dating-and-detecting-turning-points-in-the-euro-1qk27h6mkq.pdf

Laurent Ferrara

Papers

Are Disaggregate Data Useful for Factor Analysis in Forecasting French GDP

Housing Cycles in the Major Euro Area Countries

Forecasting with k‐factor Gegenbauer Processes: Theory and Applications

A System for Dating and Detecting Turning Points in the Euro Area

A three-regime real-time indicator for the US economy