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Test for the Error Component Model in the Presence of Local Misspecification

Anil Bera, Walter Sosa Escudero () and Mann Yoon

Department of Economics, Working Papers from Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata

Abstract: It is well known that most of the standard speci¯cation tests are not valid when the alternative hypothesis is misspeci¯ed. This is particularly true in the error component model, when one tests for either random e®ects or serial correlation without taking account of the presence of the other e®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 misspeci¯cation. These tests are adversely a®ected under misspeci¯cation. We suggest simple procedures to test for random e®ects (or serial correlation) in the presence of local serial correlation (or random e®ects), and these tests require ordinary least squares residuals only. Our Monte Carlo results demonstrate that the suggested tests have good ¯nite sample properties for local misspeci¯cation, and in some cases even for far distant misspeci¯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.

Date: 2000-03
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Citations: View citations in EconPapers (3) Track citations by RSS feed

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Related works:
Journal Article: Tests for the error component model in the presence of local misspecification (2001) Downloads
Working Paper: Tests for the Error Component Model in the Presence of Local Misspecification (2000) Downloads
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