Testing for vector autoregressive dynamics under heteroskedasticity

Christian Hafner and H. Herwartz

No EI 2002-36, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: In this paper we introduce a bootstrap procedure to test parameter restrictions in vector autoregressive models which is robust in cases of conditionally heteroskedastic error terms. The adopted wild bootstrap method does not require any parametric specification of the volatility process and takes contemporaneous error correlation implicitly into account. Via a Monte Carlo investigation empirical size and power properties of the new method are illustrated. We compare the bootstrap approach with standard procedures either ignoring heteroskedasticity or adopting a heteroskedasticity consistent estimation of the relevant covariance matrices in the spirit of the White correction. In terms of empirical size the proposed method clearly outperforms competing approaches without paying any price in terms of size adjusted power. We apply the alternative tests to investigate the potential of causal relationships linking daily prices of natural gas and crude oil. Unlike standard inference ignoring time varying error variances, heteroskedasticity consistent test procedures do not deliver any evidence in favor of short run causality between the two series.

Keywords: Bootstrap; Causality; Energy markets; Heteroskededasticity; Hypothesis testing; Vector autoregression (search for similar items in EconPapers)
Date: 2002-10-09
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Citations: View citations in EconPapers (2)

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Working Paper: Testing for vector autoregressive dynamics under heteroskedasticity (2002)
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