Structural vector autoregressive analysis

by Kilian, Lutz

Type Book

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Title Statements

Full title :Structural vector autoregressive analysis / Lutz Kilian, Helmut Lütkepohl.

Summary

Structural vector autoregressive (VAR) models are important tools for empirical work in macroeconomics, finance, and related fields. This book not only reviews the many alternative structural VAR approaches discussed in the literature, but also highlights their pros and cons in practice. It provides guidance to empirical researchers as to the most appropriate modeling choices, methods of estimating, and evaluating structural VAR models. The book traces the evolution of the structural VAR methodology and contrasts it with other common methodologies, including dynamic stochastic general equilibrium (DSGE) models. It is intended as a bridge between the often quite technical econometric literature on structural VAR modeling and the needs of empirical researchers. The focus is not on providing the most rigorous theoretical arguments, but on enhancing the reader's understanding of the methods in question and their assumptions. Empirical examples are provided for illustration.

Edition Statements

Publication Date:2017.

Publication Statements

Publisher(s):Cambridge University Press
Place of Publication:Cambridge, United Kingdom
Language:eng
Publication Date:2017.

The Physical Object

Description:1 online resource

Edition Identifiers

ISBN:9781108164818, 1108164811
oclc:on1035515446
LC Classification:HB141
Dewey Decimal:330.01519536

Cover -- Half title -- Series page -- Title -- Copyright -- Contents -- Preface -- 1 Introduction -- 1.1 Overview -- 1.2 Outline of the Book -- 2 Vector Autoregressive Models -- 2.1 Stationary and Trending Processes -- 2.2 Linear VAR Processes -- 2.2.1 The Basic Model -- 2.2.2 The Moving Average Representation -- 2.2.3 VAR Models as an Approximation to VARMA Processes -- 2.2.4 Marginal Processes, Measurement Errors, Aggregation, Variable Transformations -- 2.3 Estimation of VAR Models -- 2.3.1 Least-Squares Estimation -- 2.3.2 Restricted Generalized Least Squares -- 2.3.3 Bias-Corrected LS -- 2.3.4 Maximum Likelihood Estimation -- 2.3.5 VAR Processes in Levels with Integrated Variables -- 2.3.6 Sieve Autoregressions -- 2.4 Prediction -- 2.4.1 Predicting from Known VAR Processes -- 2.4.2 Predicting from Estimated VAR Processes -- 2.5 Granger Causality Analysis -- 2.6 Lag-Order Selection Procedures -- 2.6.1 Top-Down Sequential Testing -- 2.6.2 Bottom-Up Sequential Testing -- 2.6.3 Information Criteria -- 2.6.4 Recursive Mean-Squared Prediction Error Rankings -- 2.6.5 The Relative Merits of Alternative Lag-Order Selection Tools -- 2.7 Model Diagnostics -- 2.7.1 Tests for Autocorrelat…