Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models

21 Pages Posted: 14 Feb 2018

See all articles by Liudas Giraitis

Liudas Giraitis

Queen Mary

George Kapetanios

King's College, London

Tony Yates

University of Bristol

Date Written: March 2018

Abstract

In this article, we introduce the general setting of a multivariate time series autoregressive model with stochastic time‐varying coefficients and time‐varying conditional variance of the error process. This allows modelling VAR dynamics for non‐stationary time series and estimation of time‐varying parameter processes by the well‐known rolling regression estimation techniques. We establish consistency, convergence rates, and asymptotic normality for kernel estimators of the paths of coefficient processes and provide pointwise valid standard errors. The method is applied to a popular seven‐variable dataset to analyse evidence of time variation in empirical objects of interest for the DSGE (dynamic stochastic general equilibrium) literature.

Keywords: Time varying estimation, random coefficient models

Suggested Citation

Giraitis, Liudas and Kapetanios, George and Yates, Tony, Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models (March 2018). Journal of Time Series Analysis, Vol. 39, Issue 2, pp. 129-149, 2018, Available at SSRN: https://ssrn.com/abstract=3122740 or http://dx.doi.org/10.1111/jtsa.12271

Liudas Giraitis (Contact Author)

Queen Mary ( email )

Mile End Road
London, London E1 4NS
United Kingdom

HOME PAGE: http://www.econ.qmul.ac.uk/people/liudas-giraitis

George Kapetanios

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

Tony Yates

University of Bristol ( email )

University of Bristol,
Senate House, Tyndall Avenue
Bristol, BS8 ITH
United Kingdom

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