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

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  • Phillips, Peter C.B.
  • Sun, Yixiao
  • Jin, Sainan

Abstract

Sharp origin kernels, constructed by taking powers of the Bartlett kernel, are suggested for use in heteroskedasticity and autocorrelation consistent (HAC) estimation with no truncation (or bandwidth) parameter. When the power parameter (rho) is fixed, 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. When the power parameter is passed to infinity with the sample size (T), the new kernels provide consistent HAC estimates. A data-driven method for selecting the power parameter is recommended for hypothesis testing. A new test procedure that combines the good elements of fixed rho and large rho asymptotics is suggested. Simulations show that the new test is less size-distorted than the conventional HAC t-test at the cost of a very small power loss.

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Bibliographic Info

Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt6d36x00z.

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Date of creation: 28 Sep 2004
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Handle: RePEc:cdl:ucsdec:qt6d36x00z

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

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Cited by:
  1. Jen-Je Su, 2005. "On the size and power of testing for no autocorrelation under weak assumptions," Applied Financial Economics, Taylor & Francis Journals, vol. 15(4), pages 247-257.
  2. Richard Smith, 2004. "Automatic positive semi-definite HAC covariance matrix and GMM estimation," CeMMAP working papers CWP17/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. João Valle e Azevedo, 2011. "Rational vs. professional forecasts," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
  4. 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.
  5. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May.
  6. 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., vol. 23(1), pages 91-109.
  7. Peter C.B. Phillips & Yixiao Sun & Sainan Jin, 2005. "Improved HAR Inference," Cowles Foundation Discussion Papers 1513, Cowles Foundation for Research in Economics, Yale University.

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