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Testing for co-integration in vector autoregressions with non-stationary volatility

Giuseppe Cavaliere (), Anders Rahbek and Robert Taylor

Discussion Papers from University of Nottingham, Granger Centre for Time Series Econometrics

Abstract: Many key macro-economic and financial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with nonstationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special cases. We show that the conventional rank statistics of Johansen (1988,1991) are potentially unreliable. In particular, their large sample distributions depend on the integrated covariation of the underlying multivariate volatility process which impacts on both the size and power of the associated co-integration tests, as we demonstrate numerically. A solution to the identified inference problem is provided by considering wild bootstrap-based implementations of the rank tests. These 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. The bootstrap is shown to perform remarkably well in practice.

Keywords: Cointegration; non-stationary volatility; trace and maximum eigenvalue tests; wild bootstrap (search for similar items in EconPapers)
Date: 2007-08
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Related works:
Journal Article: Testing for co-integration in vector autoregressions with non-stationary volatility (2010) Downloads
Working Paper: Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility (2008) Downloads
Working Paper: Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility (2008) Downloads
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