Random Cell Chi-Square Diagnostic Tests for Econometric Models: II. Theory
Abstract
This paper extends the Pearson chi-square testing method to non-dynamic parametric econometric models, in particular, to models with covariates. The paper establishes the asymptotic distribution of the test statistic under the null and local alternatives, when the test statistic is based on data-dependent random cells of a general form, and on an arbitrary asymptotically normal estimator. These results are attained by extending recent probabilistic results for the weak convergence of empirical processes indexed by sets. The chi-square test that is introduced can be used to test goodness-of-fit of a parametric model, as well as to test particular aspects of the parametric model that are of interest. In the event of rejection of the null hypothesis, the test provides information concerning the direction of departure from the null. The diagnostics provided by the test are intuitive and particularly easy to interpret.