Tests for the error component model in the presence of local misspecification
By Anil K. Bera, Walter Sosa Escudero, M. Yoon. 2001
93 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/646ea7e237d4a63e08687103912661545c384f37
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Citations in the corpus
- Poverty Reduction and Growth: Virtuous and Vicious Circles (2006, background): “See Cortés Conde (1994, 1997) and Della Paolera, Taylor, and Bózolli (2003) for Argentina; GRECO (2002) for Colombia; Díaz, Lüders, and Wagner (1998) for Chile; and Bértola (1998) for Uruguay.”
- Panel data econometrics in R: The plm package (2008, background): “Bera, Sosa-Escudero, and Yoon (2001) derive locally robust tests both for individual random effects and for first-order serial correlation in residuals as “corrected” versions of the standard LM test (see plmtest).”