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

Matrix Differential Calculus with Applications in Statistics and Econometrics

Nowcasting: the real time informational content of macroeconomic data releases

Time series analysis and its applications

This article describes a large, monthly frequency, macroeconomic database with the goal of establishing a convenient starting point for empirical analysis that requires “big data.” The dataset mimics the coverage of those already used in the literature but has three appealing features. First, it is designed to be updated monthly using the Federal Reserve Economic Data (FRED) database. Second, it will be publicly accessible, facilitating comparison of related research and replication of empirical work. Third, it will relieve researchers from having to manage data changes and revisions. We show that factors extracted from our dataset share the same predictive content as those based on various vintages of the so-called Stock–Watson dataset. In addition, we suggest that diffusion indexes constructed as the partial sum of the factor estimates can potentially be useful for the study of business cycle chronology. Supplementary materials for this article are available online.

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FRED-MD: A Monthly Database for Macroeconomic Research

For an approximate factor model, in a static representation, with a common component comprising global factors and factors specific to groups of variables, the consistency of the principal components estimator is discussed. An extension of the well known Bai and Ng criteria is proposed for determining the number of global and group-specific factors. The consistency of the suggested criteria is established and the small sample properties are assessed through Monte Carlo simulations. As an empirical illustration, the proposed criteria is applied to estimate the number of global and country-specific macroeconomic factors for the major euro area countries.

Determining the number of factors in approximate factor models with global and group-specific factors

Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence

In this paper, we propose a quantitative measure for inflation expectations based on consumer survey data. Thereafter, we proceed to testing the rationality assumption. This issue is of noteworthy interest in its own as it is commonly assumed in the theoretical modelling literature that the rational expectations hypothesis holds. This analysis is conducted for the euro area as a whole, as well as for several member countries, using a sample covering the last two decades. Moreover, we also assess if the conclusions hold when one focuses on the post-euro introduction period.

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Inflation expectations in the euro area: are consumers rational?

There has been a growing interest on inflation perceptions in the euro area, in particular, following the euro cash changeover. It has been pointed out that a gap emerged between observed and perceived inflation since the introduction of the euro notes and coins. Such a statement relies on the fact that inflation perceptions, measured by the well-known balance statistic from the European Commission’s consumer survey, hiked after January 2002 and remained high thereafter, as opposed to the observed inflation, which has remained fairly stable. In this paper, we discuss the measurement of inflation perceptions, by comparing the balance statistic with a refined alternative measure, which is computed using the probability method. We argue that the balance statistic should be used carefully, as it can lead to misleading conclusions. In fact, we find no evidence, both for euro area and individual countries, of the breakdown in the relationship between observed and perceived inflation, as measured by the proposed alternative measure, at the time of the euro cash changeover.

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Inflation (mis)perceptions in the euro area

The simplicity of the standard diffusion index model of Stock and Watson has certainly contributed to its success among practitioners, resulting in a growing body of literature on factor-augmented forecasts. However, as pointed out by Bai and Ng, the ranked factors considered in the forecasting equation depend neither on the variable to be forecast nor on the forecasting horizon. We propose a refinement of the standard approach that retains the computational simplicity while coping with this limitation. Our approach consists of generating a weighted average of all the principal components, the weights depending both on the eigenvalues of the sample correlation matrix and on the covariance between the estimated factor and the targeted variable at the relevant horizon. This ‘targeted diffusion index’ approach is applied to US data and the results show that it outperforms considerably the standard approach in forecasting several major macroeconomic series. Moreover, the improvement is more significant in the final part of the forecasting evaluation period. Copyright © 2009 John Wiley & Sons, Ltd.

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Francisco Craveiro Dias

Papers

Determining the number of factors in approximate factor models with global and group-specific factors

Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence

Inflation expectations in the euro area: are consumers rational?

Inflation (mis)perceptions in the euro area

Forecasting using targeted diffusion indexes