Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We consider testing for autoregressive disturbances in the linear regression model with a lagged dependent variable. An approximation to the null distribution of the Durbin—Watson statistic is developed using small-disturbance asymptotics, and is used to obtain test critical values. We also obtain nonsimilar critical values for the Durbin—Watson and Durbin's h and t tests. Monte Carlo results are reported comparing the performances of the tests under the null and alternative hypotheses. The Durbin–Watson test is found to be more powerful and to perform more consistently than either of Durbin's tests under Ho.