Durbin-watson Tests for Serial Correlation in Regressions with Missing Observations*

We study two Durbin-Watson type tests for serial correlation of errors in regression models when observations are missing. We derive them by applying standard methods used in time series and linear models to deal with missing observations. The first test may be viewed as a regular Durbin-Watson test in the context of an extended model. We discuss appropriate adjustments that allow one to use all available bounds tables. We show that the test is locally most powerful invariant against the same alternative error distribution as the Durbin-Watson test. The second test is based on a modified Durbin-Watson statistic suggested by Ring (1981a) and is locally most powerful invariant against a first-order autoregressive process.