Multivariate Modelling of Non-Stationary Economic Time Series

John Hunter, Simon P. Burke, Alessandra Canepa

• Publisher: Palgrave Macmillan
• Year: 2017
• Print ISBN: 9780230243309
• eBook ISBN: 9781137313034
• Edition: 2
• Topic: Economics
• Subtopic: Econometrics
• Index: Economics

This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction, volatility and the impact of different orders of integration. Models with expectations are considered along with alternate methods such as Singular Spectrum Analysis (SSA), the Kalman Filter and Structural Time Series, all in relation to cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide direction and guidance to the now vast literature facing students and graduate economists.

Table of contents

• Preface
• Contents
• 1 Introduction
• 2 Multivariate Time Series
• 3 Cointegration
• 4 Testing for Cointegration: Standard and Non-Standard Conditions
• 5 Structure and Evaluation
• 6 Testing in VECMs with Small Samples
• 7 Heteroscedasticity and Multivariate Volatility
• 8 Models with Alternative Orders of Integration
• 9 The Structural Analysis of Time Series
• Appendix A Matrix Preliminaries
• Appendix B Matrix Algebra for B:Engle and Granger1987 Representation
• Appendix C Johansen's Procedure as a Maximum Likelihood Procedure
• Appendix D The Maximum Likelihood Procedure in Terms of Canonical Correlations
• Appendix E Distribution Theory
• Appendix F Estimation Under General Restrictions
• Appendix G Proof of Identification Based on an Indirect Solution
• Appendix H Generic Identification of Long-Run Parameters in Sect.5.3
• Appendix I IRF MA Parameters for the Case in Sect.5.4.3
• References
• Bibliography
• Index