In this paper the bootstrap theories,which are based on the author’s former paper,ofM-typ eprincipal components and dispersion matrices and M-type PP tests for multivariate locationand scale are obtained.The bootstra...In this paper the bootstrap theories,which are based on the author’s former paper,ofM-typ eprincipal components and dispersion matrices and M-type PP tests for multivariate locationand scale are obtained.The bootstrap confidence sets for the principal components,dispersionmatrices and correlation matrices are also constructed.展开更多
Testing the equality of percentiles (quantiles) between populations is an effective method for robust, nonparametric comparison, especially when the distributions are asymmetric or irregularly shaped. Unlike global no...Testing the equality of percentiles (quantiles) between populations is an effective method for robust, nonparametric comparison, especially when the distributions are asymmetric or irregularly shaped. Unlike global nonparametric tests for homogeneity such as the Kolmogorv-Smirnov test, testing the equality of a set of percentiles (i.e., a percentile profile) yields an estimate of the location and extent of the differences between the populations along the entire domain. The Wald test using bootstrap estimates of variance of the order statistics provides a unified method for hypothesis testing of functions of the population percentiles. Simulation studies are conducted to show performance of the method under various scenarios and to give suggestions on its use. Several examples are given to illustrate some useful applications to real data.展开更多
Testing the validity of the conditional capital asset pricing model （CAPM） is a puzzle in the finance literature. Lewellen and Nagel[14] find that the variation in betas and in the equity premium would have to be im...Testing the validity of the conditional capital asset pricing model （CAPM） is a puzzle in the finance literature. Lewellen and Nagel[14] find that the variation in betas and in the equity premium would have to be implausibly large to explain important asset-pricing anomalies. Unfortunately, they do not provide a rigorous test statistic. Based on a simulation study, the method proposed in Lewellen and Nagel[14] tends to reject the null too frequently. We develop a new test procedure and derive its limiting distribution under the null hypothesis. Also, we provide a Bootstrap approach to the testing procedure to gain a good finite sample performance. Both simulations and empirical studies show that our test is necessary for making correct inferences with the conditional CAPM.展开更多
基金The research was supported in part by NBF grants of China
文摘In this paper the bootstrap theories,which are based on the author’s former paper,ofM-typ eprincipal components and dispersion matrices and M-type PP tests for multivariate locationand scale are obtained.The bootstrap confidence sets for the principal components,dispersionmatrices and correlation matrices are also constructed.
文摘Testing the equality of percentiles (quantiles) between populations is an effective method for robust, nonparametric comparison, especially when the distributions are asymmetric or irregularly shaped. Unlike global nonparametric tests for homogeneity such as the Kolmogorv-Smirnov test, testing the equality of a set of percentiles (i.e., a percentile profile) yields an estimate of the location and extent of the differences between the populations along the entire domain. The Wald test using bootstrap estimates of variance of the order statistics provides a unified method for hypothesis testing of functions of the population percentiles. Simulation studies are conducted to show performance of the method under various scenarios and to give suggestions on its use. Several examples are given to illustrate some useful applications to real data.
基金the National Nature Science Foundation of China(71131008(Key Project),70871003,70971113)supported by the Fundamental Research Funds for the Central Universities(2013221022)+1 种基金the Natural Science Foundation of Fujian Province(2011J01384)the Natural Science Foundation of China(71301135,71203189,71131008)
文摘Testing the validity of the conditional capital asset pricing model （CAPM） is a puzzle in the finance literature. Lewellen and Nagel[14] find that the variation in betas and in the equity premium would have to be implausibly large to explain important asset-pricing anomalies. Unfortunately, they do not provide a rigorous test statistic. Based on a simulation study, the method proposed in Lewellen and Nagel[14] tends to reject the null too frequently. We develop a new test procedure and derive its limiting distribution under the null hypothesis. Also, we provide a Bootstrap approach to the testing procedure to gain a good finite sample performance. Both simulations and empirical studies show that our test is necessary for making correct inferences with the conditional CAPM.
