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Full-Text Articles in Economics
Long Run Variance Estimation Using Steep Origin Kernels Without Truncation, Peter C.B. Phillips, Yixiao Sun, Sainan Jin
Long Run Variance Estimation Using Steep Origin Kernels Without Truncation, Peter C.B. Phillips, Yixiao Sun, Sainan Jin
Cowles Foundation Discussion Papers
A new class of kernel estimates is proposed for long run variance (LRV) and heteroskedastic autocorrelation consistent (HAC) estimation. The kernels are called steep origin kernels and are related to a class of sharp origin kernels explored by the authors (2003) in other work. They are constructed by exponentiating a mother kernel (a conventional lag kernel that is smooth at the origin) and they can be used without truncation or bandwidth parameters. When the exponent is passed to infinity with the sample size, these kernels produce consistent LRV/HAC estimates. The new estimates are shown to have limit normal distributions, and …
Prewhitening Bias In Hac Estimation, Donggyu Sul, Peter C.B. Phillips, Chi-Young Choi
Prewhitening Bias In Hac Estimation, Donggyu Sul, Peter C.B. Phillips, Chi-Young Choi
Cowles Foundation Discussion Papers
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 recoloring 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, greater than 0.97 be replaced by …
Consistent Hac Estimation And Robust Regression Testing Using Sharp Origin Kernels With No Truncation, Peter C.B. Phillips, Yixiao Sun, Sainan Jin
Consistent Hac Estimation And Robust Regression Testing Using Sharp Origin Kernels With No Truncation, Peter C.B. Phillips, Yixiao Sun, Sainan Jin
Cowles Foundation Discussion Papers
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. As the power parameter (ρ) increases, the kernels become very sharp at the origin and increasingly downweight values away from the origin, thereby achieving effects similar to a bandwidth parameter. Sharp origin kernels can be used in regression testing in much the same way as conventional kernels with no truncation, as suggested …