  Testing for series correlation among error terms is a basic problem in linear
regression model diagnostics. The famous Durbin-Watson test and Durbin's h-test
rely on certain model assumptions about the response and regressor variables.
The present paper proposes simple tests for series correlation that are
applicable in both fixed and random design linear regression models. The test
statistics are based on the regression residuals and design matrix. The test
procedures are robust under different distributions of random errors. The
asymptotic distributions of the proposed statistics are derived via a newly
established joint central limit theorem for several general quadratic forms and
the delta method. Good performance of the proposed tests is demonstrated by
simulation results.
