Alternative approaches to testing non—nested models with auto correlated disturbances

Since departures from the classical assumptions regarding the disturbances in a linear regression model arise frequently in empirical applications, several computationally straightforward procedures are presented in this paper for testing non—nested models when the disturbances of these models follow first— or higher—order autoregressive processes. An empirical example is used to illustrate how the procedures may be used to test competing Keynesian and New Classical non—nested models of unemployment for the U.S. using annual time series data for 1955—85.