Alternative non-nested specification tests of time-series investment models

This paper develops and compares non-nested hypothesis tests for linear regression models with first-order serially correlated errors. It extends the non-nested testing procedures of Pesaran, Fisher and McAleer, and Davidson and MacKinnon, and compares their performance on four conventional models of aggregate investment demand using quarterly U.S. investment data from 1951:I to 1983:IV. The data and the non-nested hypothesis tests initially indicate that none of the models are correctly specified. The tests are also intransitive in their assessments. Before rejecting these conventional models of investment demand, we investigate the small-sample properties of these different non-nested test procedures through a series of Monte Carlo studies. We find that these non-nested tests can have significant finite-sample size and power biases.