Tests for the error component model in the presence of local misspecification

It is well known that most of the standard specification tests are not vali d when the alternative hypothesis is misspecified. This is particularly tru e in the error component model, when one tests for either random effects or serial correlation without taking account of the presence of the other eff ect. In this paper we study the size and power of the standard Rao's score tests analytically and by simulation when the data are contaminated by loca l misspecification. These tests are adversely affected under misspecificati on. We suggest simple procedures to test for random effects (or serial corr elation) in the presence of local serial correlation (or random effects), a nd these tests require ordinary least-squares residuals only. Our Monte Car lo results demonstrate that the suggested tests have good finite sample pro perties for local misspecification, and in some cases even for far distant misspecification. Our tests are also capable of detecting the right directi on of the departure from the null hypothesis. We also provide some empirica l illustrations to highlight the usefulness of our tests. (C) 2001 Elsevier Science S.A. All rights reserved. JEL classification: C12; C23; C52.