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Full-Text Articles in Social and Behavioral Sciences
Power Maximization And Size Control Of Heteroscedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Power Maximization And Size Control Of Heteroscedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Research Collection School Of Economics
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 (ρ). We show that the nonstandard fixed-ρ limit distributions of the t-statistic provide more accurate approximations to the finite sample distributions than the conventional large-ρ 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-ρ asymptotics. As a further contribution, we propose a new practical procedure for selecting the test-optimal power parameter that addresses the central …
Power Maximization And Size Control In Heteroskedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Power Maximization And Size Control In Heteroskedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Research Collection School Of Economics
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 …
Optimal Bandwidth Selection In Heteroskedasticity-Autocorrelation Robust Testing, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Optimal Bandwidth Selection In Heteroskedasticity-Autocorrelation Robust Testing, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Research Collection School Of Economics
This paper considers studentized tests in time series regressions with nonparametrically autocorrelated errors. The studentization is based on robust standard errors with truncation lag M = bT for some constant b ∈ (0, 1] and sample size T. It is shown that the nonstandard fixed-b limit distributions of such nonparametrically studentized tests provide more accurate approximations to the finite sample distributions than the standard small-b limit distribution. We further show that, for typical economic time series, the optimal bandwidth that minimizes a weighted average of type I and type II errors is larger by an order of magnitude than the …
A New Statistic For Regression Transformation, Zhenlin Yang
A New Statistic For Regression Transformation, Zhenlin Yang
Research Collection School Of Economics
A new statistic for testing a regression transformation is proposed based on a result of Yang (1999). This statistic is shown to be stable, having a null distribution almost independent of model type and parameter values, accurate and easy to implement. The statistic is of the Wald-type and thus is compared with the Wald statistic given by Lawrence (1987) in terms of size, null distribution and power using simulation. The simulation results show that the new statistic generally outperforms that of Lawrence.
Estimating A Transformation And Its Effect On Box-Cox T-Ratio, Zhenlin Yang
Estimating A Transformation And Its Effect On Box-Cox T-Ratio, Zhenlin Yang
Research Collection School Of Economics
This article concerns i) the stochastic behavior of the Box-Cox transformation estimator and ii) the effect of estimating a transformation on the Box-CoxT-ratio used for the post-transformation analysis. It is shown that the transformation estimator depends on three factors: the model structure, the mean-spread and the error standard deviation σ0. In general, a structured model is able to estimate the transformation very well; an unstructured model can do well also unless the mean-spread and σ0 are both small; and a one-mean mode can give a poor-estimate if σ0 is small. When the sample is not large, it is shown that …