Conflict Among Testing Procedures in a Linear Regression Model with Autoregressive Disturbances

Abstract

Silvey [10]. For a model with nonstochastic regressors we show that a systematic inequality relation exists among the test statistics; namely, the value of the Wald statistic is greater than or equal to that of the LR statistic which, in turn, is greater than or equal to that of the LM statistic. When the null hypothesis is true, we find that the Wald, LR, and LM test statistics have identical limiting chi-square distributions. Since for a large sample test the three procedures employ the same critical region, the inequality relation among the test statistics implies that there exists a significance level such that the tests will produce conflicting inferences. These results are parallel to those obtained by Berndt and Savin [2] in the context of a multivariate regression model with independent disturbance vectors. We also consider the Wald and LR tests for a model with a lagged dependent variable. In this case the Wald statistic is not the same as in the nonstochastic regressor case with the result that the inequality between the Wald and LR test statistics no longer holds. We conclude the paper with an empirical example which illustrates the relation among the test statistics.