A Joint Test for Conditional Heteroscedasticity in Dynamic Panel Data Models

This article proposes a joint test for conditional heteroscedasticity in dynamic panel data models. The test is constructed by checking the joint significance of estimates of second to pth-order serial correlation in the squares sequence of the first differenced errors. To avoid any distribution assumptions of the errors and the effects, we adopt the GMM estimation for the parameter coefficient and higher order moment estimation for the errors. Based on the estimations, a joint test is constructed for conditional heteroscedasticity in the error. The resulted test is asymptotically chi-squared under the null hypothesis and easy to implement. The small sample properties of the test are investigated by means of Monte Carlo experiments. The evidence shows that the test performs well in dynamic panel data with large number n of individuals and short periods T of time. A real data is analyzed for illustration.