This paper suggests a modified serial correlation test for linear panel data models, which is based on the parameter estimates for an artificial autoregression modeled by differencing and centering residual vectors. S...This paper suggests a modified serial correlation test for linear panel data models, which is based on the parameter estimates for an artificial autoregression modeled by differencing and centering residual vectors. Specifically, the differencing operator over the time index and the centering operator over the individual index are, respectively, used to eliminate the potential individual effects and time effects so that the resultant serial correlation test is robust to the two potential effects. Clearly, the test is also robust to the potential correlation between the covariates and the random effects. The test is asymptotically chi-squared distributed under the null hypothesis. Power study shows that the test can detect local alternatives distinct at the parametric rate from the null hypothesis. The finite sample properties of the test are investigated by means of Monte Carlo simulation experiments, and a real data example is analyzed for illustration.展开更多
基金Supported by the National Nature Science Foundation of China(Grant No.11001238)the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20103326120002)+3 种基金the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities(Grant No.13JJD910002)the National Bureau of statistics of key projects(Grant No.2012LZ023)the Zhejiang Provincial Key Research Base for Humanities and Social Science Research(Statistics)the Center for Studies of Modern Business Zhejiang Gongshang University in the key research base for humanities and social Sciences of the Ministry of Education(Grant No.12JDSM09YB)
文摘This paper suggests a modified serial correlation test for linear panel data models, which is based on the parameter estimates for an artificial autoregression modeled by differencing and centering residual vectors. Specifically, the differencing operator over the time index and the centering operator over the individual index are, respectively, used to eliminate the potential individual effects and time effects so that the resultant serial correlation test is robust to the two potential effects. Clearly, the test is also robust to the potential correlation between the covariates and the random effects. The test is asymptotically chi-squared distributed under the null hypothesis. Power study shows that the test can detect local alternatives distinct at the parametric rate from the null hypothesis. The finite sample properties of the test are investigated by means of Monte Carlo simulation experiments, and a real data example is analyzed for illustration.
