Consistent HAC Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation
AbstractSharp 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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Date of creation: 28 Sep 2004
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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, 2004. "Consistent HAC Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation," Econometric Society 2004 North American Winter Meetings 299, Econometric Society.
- Peter C.B. Phillips & Sainan Jin & Yixiao Sun, 2004. "Consistent HAC Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation," Yale School of Management Working Papers ysm347, Yale School of Management.
- Peter C.B. Phillips & Yixiao Sun & Sainan Jin, 2003. "Consistent HAC Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation," Cowles Foundation Discussion Papers 1407, Cowles Foundation for Research in Economics, Yale University.
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- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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