Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels

Using the power kernels of Phillips, Sun and Jin (2006, 2007), we examine the large sample asymptotic properties of the t-test for different choices of power parameter (rho). We show that the nonstandard fixed-rho limit distributions of the t-statistic provide more accurate approximations to the fin...

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Main Authors: SUN, Yixiao, Phillips, Peter C. B., JIN, Sainan
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Language:English
Published: Institutional Knowledge at Singapore Management University 2009
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Online Access:https://ink.library.smu.edu.sg/soe_research/1166
https://ink.library.smu.edu.sg/context/soe_research/article/2165/viewcontent/PowerMaxSizeControl_2009_wp.pdf
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spelling sg-smu-ink.soe_research-21652018-05-18T05:27:09Z Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels SUN, Yixiao Phillips, Peter C. B. JIN, Sainan Using the power kernels of Phillips, Sun and Jin (2006, 2007), we examine the large sample asymptotic properties of the t-test for different choices of power parameter (rho). We show that the nonstandard fixed-rho limit distributions of the t-statistic provide more accurate approximations to the finite sample distributions than the conventional large-rho limit distribution. We prove that the second-order corrected critical value based on an asymptotic expansion of the nonstandard limit distribution is also second-order correct under the large-rho asymptotics. As a further contribution, we propose a new practical procedure for selecting the test-optimal power parameter that addresses the central concern of hypothesis testing: the selected power parameter is test-optimal in the sense that it minimizes the type II error while controlling for the type I error. A plug-in procedure for implementing the test-optimal power parameter is suggested. Simulations indicate that the new test is as accurate in size as the nonstandard test of Kiefer and Vogelsang (2002a, 2002b; KV), and yet it does not incur the power loss that often hurts the performance of the latter test. The new test therefore combines the advantages of the KV test and the standard (MSE optimal) HAC test while avoiding their main disadvantages (power loss and size distortion, respectively). The results complement recent work by Sun, Phillips and Jin (2008) on conventional and bT HAC testing. 2009-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1166 https://ink.library.smu.edu.sg/context/soe_research/article/2165/viewcontent/PowerMaxSizeControl_2009_wp.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Asymptotic expansion HAC estimation Long run variance Loss function Optimal smoothing parameter Power kernel Power maximization Size control Type I error Type II error Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Asymptotic expansion
HAC estimation
Long run variance
Loss function
Optimal smoothing parameter
Power kernel
Power maximization
Size control
Type I error
Type II error
Econometrics
spellingShingle Asymptotic expansion
HAC estimation
Long run variance
Loss function
Optimal smoothing parameter
Power kernel
Power maximization
Size control
Type I error
Type II error
Econometrics
SUN, Yixiao
Phillips, Peter C. B.
JIN, Sainan
Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels
description Using the power kernels of Phillips, Sun and Jin (2006, 2007), we examine the large sample asymptotic properties of the t-test for different choices of power parameter (rho). We show that the nonstandard fixed-rho limit distributions of the t-statistic provide more accurate approximations to the finite sample distributions than the conventional large-rho limit distribution. We prove that the second-order corrected critical value based on an asymptotic expansion of the nonstandard limit distribution is also second-order correct under the large-rho asymptotics. As a further contribution, we propose a new practical procedure for selecting the test-optimal power parameter that addresses the central concern of hypothesis testing: the selected power parameter is test-optimal in the sense that it minimizes the type II error while controlling for the type I error. A plug-in procedure for implementing the test-optimal power parameter is suggested. Simulations indicate that the new test is as accurate in size as the nonstandard test of Kiefer and Vogelsang (2002a, 2002b; KV), and yet it does not incur the power loss that often hurts the performance of the latter test. The new test therefore combines the advantages of the KV test and the standard (MSE optimal) HAC test while avoiding their main disadvantages (power loss and size distortion, respectively). The results complement recent work by Sun, Phillips and Jin (2008) on conventional and bT HAC testing.
format text
author SUN, Yixiao
Phillips, Peter C. B.
JIN, Sainan
author_facet SUN, Yixiao
Phillips, Peter C. B.
JIN, Sainan
author_sort SUN, Yixiao
title Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels
title_short Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels
title_full Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels
title_fullStr Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels
title_full_unstemmed Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels
title_sort power maximization and size control in heteroskedasticity and autocorrelation robust tests with exponentiated kernels
publisher Institutional Knowledge at Singapore Management University
publishDate 2009
url https://ink.library.smu.edu.sg/soe_research/1166
https://ink.library.smu.edu.sg/context/soe_research/article/2165/viewcontent/PowerMaxSizeControl_2009_wp.pdf
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