Prewhitening Bias in HAC Estimation
AbstractHeteroskedasticity and autocorrelation consistent (HAC) estimation commonly involves the use of prewhitening filters based on simple autoregressive models. In such applications, small sample bias in the estimation of autoregressive coefficients is transmitted to the recolouring filter, leading to HAC variance estimates that can be badly biased. The present paper provides an analysis of these issues using asymptotic expansions and simulations. The approach we recommend involves the use of recursive demeaning procedures that mitigate the effects of small-sample autoregressive bias. Moreover, a commonly used restriction rule on the prewhitening estimates (that first-order autoregressive coefficient estimates, or largest eigenvalues, >0.97 be replaced by 0.97) adversely interferes with the power of unit-root and [Kwiatkowski, Phillips, Schmidt and Shin (1992) "Journal of Econometrics", Vol. 54, pp. 159-178] (KPSS) tests. We provide a new boundary condition rule that improves the size and power properties of these tests. Some illustrations of the effects of these adjustments on the size and power of KPSS testing are given. Using prewhitened HAC estimates and the new boundary condition rule, the KPSS test is consistent, in contrast to KPSS testing that uses conventional prewhitened HAC estimates [Lee, J. S. (1996) "Economics Letters", Vol. 51, pp. 131-137]. Copyright 2005 Blackwell Publishing Ltd.
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Bibliographic InfoArticle provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics & Statistics.
Volume (Year): 67 (2005)
Issue (Month): 4 (08)
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Other versions of this item:
- Donggyu Sul & Peter C.B. Phillips & Choi, Chi-Young, 2003. "Prewhitening Bias in HAC Estimation," Cowles Foundation Discussion Papers 1436, Cowles Foundation for Research in Economics, Yale University.
- Peter C.B. Phillips & Chi-Young Choi & Donggyu Sul, 2004. "Prewhitening Bias in HAC Estimation," Yale School of Management Working Papers ysm426, Yale School of Management.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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