Tests for the Error Component Model in the Presence of Local Misspecification
AbstractIt is well known that most of the standard speciÂ¯cation tests are not valid when the alternative hypothesis is misspeciÂ¯ed. This is particularly true in the error component model, when one tests for either random eÂ®ects or serial correlation without taking account of the presence of the other eÂ®ect. In this paper we study the size and power of the standard Rao's score tests analytically and by simulation when the data is contaminated by local misspeciÂ¯cation. These tests are adversely aÂ®ected under misspeciÂ¯cation. We suggest simple procedures to test for random eÂ®ects (or serial correlation) in the presence of local serial correlation (or random eÂ®ects), and these tests require ordinary least squares residuals only. Our Monte Carlo results demonstrate that the suggested tests have good Â¯nite sample properties for local misspeciÂ¯cation, and in some cases even for far distant misspeciÂ¯cation. Our tests are also capable of detecting the right direction of the departure from the null hypothesis. We also provide some empirical illustrations to highlight the usefulness of our tests.
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- Bera, Anil K. & Sosa-Escudero, Walter & Yoon, Mann, 2001. "Tests for the error component model in the presence of local misspecification," Journal of Econometrics, Elsevier, vol. 101(1), pages 1-23, March.
- Anil Bera & Walter Sosa Escudero & Mann Yoon, 2000. "Test for the Error Component Model in the Presence of Local Misspecification," Department of Economics, Working Papers 022, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata.
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