Alternative Nonnested Specification Tests of Time Series Investment Models

This paper develops and compares nonnested hypothesis tests for linear regression models with first-order serially correlated errors. It extends the nonnested 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 1951:1 to 1983:IV. The data and the nonnested hypothesis tests initially indicate that no model is correctly specified, and that the tests are occasionally intransitive in their assessments. Before rejecting these conventional models of investment demand, we go on to investigate the small sample properties of these different nonnested test procedures through a series of monte carlo studies. These investigations demonstrate that when there is significant serial correlation, there are systematic finite sample biases in the nominal size and power of these test statistics. The direction of the bias is toward rejection of the null model, although it varies considerably by the type of test and estimation technique. After revising our critical levels for this finite sample bias, we conclude that the accelerator model of equipment investment cannot be rejected by any of the other alternatives.