Robustifying multivariate trend tests to nonstationary volatility

Ke-Li Xu

Journal of Econometrics, 2012, vol. 169, issue 2, 147-154

Abstract: This article studies inference of multivariate trend model when the volatility process is nonstationary. Within a quite general framework we analyze four classes of tests based on least squares estimation, one of which is robust to both weak serial correlation and nonstationary volatility. The existing multivariate trend tests, which either use non-robust standard errors or rely on non-standard distribution theory, are generally non-pivotal involving the unknown time-varying volatility function in the limit. Two-step residual-based i.i.d. bootstrap and wild bootstrap procedures are proposed for the robust tests and are shown to be asymptotically valid. Simulations demonstrate the effects of nonstationary volatility on the trend tests and the good behavior of the robust tests in finite samples.

Keywords: Bootstrap; Heteroskedasticity and autocorrelation robust inference; Multivariate trend model; Nonstationary volatility; Variance change (search for similar items in EconPapers)
JEL-codes: C12 C32 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:169:y:2012:i:2:p:147-154

DOI: 10.1016/j.jeconom.2012.01.016

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