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Consistent HAC Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation

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  • Sainan Jin
  • Peter Phillips
  • Yixiao Sun

Abstract

A new family of kernels is suggested for use in heteroskedasticity and autocorrelation consistent (HAC) and long run variance (LRV) estimation and robust regression testing. The kernels are constructed by taking powers of the Bartlett kernel and are intended to be used with no truncation (or bandwidth) parameter. The news kernels, called sharp origin kernels, can be used in regression testing in much the same way as conventional kernels with no truncation, as suggested in the work of Kiefer and Vogelsang. Analysis and simulations indicate that sharp origin kernels lead to tests with improved size properties relative to conventional tests and better power properties than other tests using Bartlett and other conventional kernels without truncation. If rho is passed to infinity with the sample size (T), the new kernels provide consistent HAC and LRV estimates as well as continued robust regression testing. Simulations show that in regression testing with the sharp origin kernel, the power properties are better than those with simple untruncated kernels (where rho =1) and at least as good as those with truncated kernels. Size is generally more accurate with sharp origin kernels than truncated kernels. In practice a simple fixed choice of the exponent parameter around rho=16 for the sharp origin kernel produces favorable results for both size and power in regression testing with sample sizes that are typical in econometric applications.

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Winter Meetings with number 299.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nawm04:299

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Keywords: Consistent HAC estimation; data determined kernel estimation; long run variance; Mercer's theorem; power parameter; sharp origin kernel.;

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Cited by:
  1. João Valle e Azevedo & João Tovar Jalles, 2011. "Rational vs. Professional Forecasts," Working Papers, Banco de Portugal, Economics and Research Department w201114, Banco de Portugal, Economics and Research Department.
  2. Bernard Fingleton & Michelle Catherine Baddeley, 2011. "Globalisation And Wage Differentials: A Spatial Analysis," Manchester School, University of Manchester, vol. 79(5), pages 1018-1034, 09.
  3. Richard Smith, 2004. "Automatic positive semi-definite HAC covariance matrix and GMM estimation," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP17/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Surajit Ray & N. E. Savin, 2008. "The performance of heteroskedasticity and autocorrelation robust tests: a Monte Carlo study with an application to the three-factor Fama-French asset-pricing model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 23(1), pages 91-109.
  5. Peter C.B. Phillips & Yixiao Sun & Sainan Jin, 2005. "Improved HAR Inference," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 1513, Cowles Foundation for Research in Economics, Yale University.
  6. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, Elsevier, vol. 132(1), pages 59-79, May.
  7. Jen-Je Su, 2005. "On the size and power of testing for no autocorrelation under weak assumptions," Applied Financial Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 15(4), pages 247-257.

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