The Variance Ratio Statistic at large Horizons

Willa Chen and Rohit Deo ()
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Willa Chen: Texas A&M University

Econometrics from University Library of Munich, Germany

Abstract: We make three contributions to using the variance ratio statistic at large horizons. Allowing for general heteroscedasticity in the data, we obtain the asymptotic distribution of the statistic when the horizon k is increasing with the sample size n but at a slower rate so that k/n → 0. The test is shown to be consistent against a variety of relevant mean reverting alternatives when k/n → 0. This is in contrast to the case when k/n → δ >0, where the statistic has been recently shown to be inconsistent against such alternatives. Secondly, we provide and justify a simple power transformation of the statistic which yields almost perfectly normally distributed statistics in finite samples, solving the well known right skewness problem. Thirdly, we provide a more powerful way of pooling information from different horizons to test for mean reverting alternatives. Monte Carlo simulations illustrate the theoretical improvements provided.

Keywords: Mean reversion; frequency domain; power transformations (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2005-01-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
Note: Type of Document - pdf; pages: 40
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Related works:
Journal Article: THE VARIANCE RATIO STATISTIC AT LARGE HORIZONS (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0501003

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