AN ENCOMPASSING APPROACH TO CONDITIONAL MEAN TESTS WITH APPLICATIONS TO TESTING NONNESTED HYPOTHESES

A general class of tests designed to detect conditional mean misspecification for cross-section or time-series applications is proposed. The tests are derived from a particular application of the encompassing principle. The resulting conditional mean encompassing (CME) tests contain as special cases a version of the Lagrange multiplier test for nested models, a new test in the presence of nonnested alternatives, and a version of the Hausman test that compares two weighted nonlinear least-squares estimators. The tests are valid without any assumption on the conditional variance of the dependent variable and can be computed using any rad T-consistent estimators. Moreover, CME tests for nonlinear dynamic models are computable from linear least-squares regressions. © 1990.