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Vector autoregressive models
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EUI ECO; 2011/30
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LUETKEPOHL, Helmut, Vector autoregressive models, EUI ECO, 2011/30 - https://hdl.handle.net/1814/19354
Abstract
Multivariate simultaneous equations models were used extensively for macroeconometric analysis when Sims (1980) advocated vector autoregressive (VAR) models as alternatives. At that time longer and more frequently observed macroeconomic time series called for models which described the dynamic structure of the variables. VAR models lend themselves for this purpose. They typically treat all variables as a priori endogenous. Thereby they account for Sims’ critique that the exogeneity assumptions for some of the variables in simultaneous equations models are ad hoc and often not backed by fully developed theories. Restrictions, including exogeneity of some of the variables, may be imposed on VAR models based on statistical procedures. VAR models are natural tools for forecasting. Their setup is such that current values of a set of variables are partly explained by past values of the variables involved. They can also be used for economic analysis, however, because they describe the joint generation mechanism of the variables involved. Structural VAR analysis attempts to investigate structural economic hypotheses with the help of VAR models. Impulse response analysis, forecast error variance decompositions, historical decompositions and the analysis of forecast scenarios are the tools which have been proposed for disentangling the relations between the variables in a VAR model. Traditionally VAR models are designed for stationary variables without time trends. Trending behavior can be captured by including deterministic polynomial terms. In the 1980s the discovery of the importance of stochastic trends in economic variables and the development of the concept of cointegration by Granger (1981), Engle and Granger (1987), Johansen (1995) and others have shown that stochastic trends can also be captured by VAR models. If there are trends in some of the variables it may be desirable to separate the long-run relations from the short-run dynamics of the generation process of a set of variables. Vector error correction models offer a convenient framework for separating longrun and short-run components of the data generation process (DGP). In the present chapter levels VAR models are considered where cointegration relations are not modelled explicitly although they may be present. Specific issues related to trending variables will be mentioned occasionally throughout the chapter. The advantage of levels VAR models over vector error correction models is that they can also be used when the cointegration structure is unknown. Cointegration analysis and error correction models are discussed specifically in the next chapter.
Table of Contents
1 Introduction
1.1 Structure of the Chapter
1.2 Terminology, Notation and General Assumptions
2 VAR Processes
2.1 The Reduced Form
2.2 Structural Forms
3 Estimation of VAR Models
3.1 Classical Estimation of Reduced Form VARs
3.2 Bayesian Estimation of Reduced Form VARs
3.3 Estimation of Structural VARs
4 Model Specification
5 Model Checking
5.1 Tests for Residual Autocorrelation
5.2 Other Popular Tests for Model Adequacy
6 Forecasting
6.1 Forecasting Known VAR Processes
6.2 Forecasting Estimated VAR Processes
7 Granger-Causality Analysis
8 Structural Analysis
8.1 Impulse Response Analysis
8.2 Forecast Error Variance Decompositions
8.3 Historical Decomposition of Time Series
8.4 Analysis of Forecast Scenarios
9 Conclusions and Extensions