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Inference on Cointegration Parameters in Vector Autoregressions with Non-Stationary Volatility




Speaker: Professor Peter Boswijk - University of Amsterdam

Series: Economics Department Seminar Series

Economics Department Seminar

Given the well-established fact that many key macro-economic and financial variables are subject to permanent changes in unconditional volatility, in this paper we consider estimation and hypothesis testing on the cointegrating relations and adjustment coefficients in vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special cases.

We show that the conventional results in Johansen (1996), i.e., the Gaussian maximum likelihood estimators of the cointegrating vectors and adjustment coefficients are (mixed) normal, with the associated likelihood ratio tests for linear restrictions being asymptotically chi-squared, break down under permanent volatility changes.

As a consequence, standard confidence intervals and tests of hypothesis are potentially unreliable. As a solution, we propose wild bootstrap inference methods which do not require the practitioner to specify a parametric model for volatility, nor to assume that the pattern of volatility is common to, or independent across, the vector of series under analysis. We formally establish that the wild bootstrap allows to replicate the relevant asymptotic distributions and that it has very good finite sample properties under a wide range of volatility models. An application to the term structure of interest rates in the US illustrates the difference between standard and bootstrap inferences regarding hypotheses on the cointegrating vectors and adjustment coefficients.

Speaker's details can be found here.

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When and where

5.00pm - 6.20pmWednesday 5th June 2013

A227 College Building City, University of London St John Street London EC1V 4PB United Kingdom