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.
We suggest the score type tests for goodness-of-fit of conditional heteroscedasticity models in both univariate and multivariate time series.The tests can detect the alternatives converging to the null at a parametric rate.Weight functions are involved in the construction of the tests,which provides us with the flexibility to choose scores,especially under directional alternatives,for enhancing power performance.Furthermore,when the alternatives are not directional,we construct asymptotically distribution-free maximin tests for a large class of alternatives.A possibility to construct score-based omnibus tests is discussed when the alternative is saturated.The power performance is also investigated.A simulation study is carried out and a real data is analyzed.
WU JianHong1,& ZHU LiXing2 1College of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018,China
