Tests for the error component model in the presence of local misspecification
Article Abstract:
It is well known that most of the standard specification tests are not valid when the alternative hypothesis is misspecified. This is particularly true in the error component model, when one tests for either random effects or serial correlation without taking account of the presence of the other effect. 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 local misspecification. These tests are adversely affected under misspecification. We suggest simple procedures to test for random effects (or serial correlation) in the presence of local serial correlation (or random effects), and these tests require ordinary least-squares residuals only. Our Monte Carlo results demonstrate that the suggested tests have good finite sample properties for local misspecification, and in some cases even for far distant misspecification. 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. [C] 2001 Elsevier Science S.A. All rights reserved. JEL classification: C12; C23; C52 Keywords: Error component model; Testing; Random effects; Serial correlation; Local misspecification
Publication Name: Journal of Econometrics
Subject: Economics
ISSN: 0304-4076
Year: 2001
User Contributions:
Comment about this article or add new information about this topic:
Causality tests and conditional heteroskedasticity: Monte Carlo evidence
Article Abstract:
This paper investigates the reliability of causality tests based on least squares when conditional heteroskedasticity exists. Monte Carlo evidence documents considerable size distortion if the conditional variances are correlated. Inference based on a heteroskedasticity and autocorrelation consistent covariance matrix offers little improvement. This size distortion traces to an inability to discriminate between causality in mean and causality in variance. As a result, this paper endorses conducting causality tests based on an empirical specification that explicitly models the conditional means and conditional variances. The relationship between money and prices serves as an illustrative example. [C] 2001 Elsevier Science S.A. All rights reserved. JEL classification: Classification: C12; C15 Keywords: Simulation; ARCH
Publication Name: Journal of Econometrics
Subject: Economics
ISSN: 0304-4076
Year: 2001
User Contributions:
Comment about this article or add new information about this topic:
- Abstracts: Computational analysis of the accession of Chile to the NAFTA and Western Hemisphere Integration. Multilateral, regional and bilateral trade-policy options for the United States and Japan
- Abstracts: Evidence from patents and patent citations on the impact of NASA and other federal labs on commercial innovation
- Abstracts: Sunspots and the business cycle in a finance constrained economy. General equilibrium when economic growth exceeds discounting
- Abstracts: The asymptotic nucleolus of large monopolistic market games. On the core of an economy with differential information
- Abstracts: The Russian economic reforms through the eyes of Western critics: the "collapse" of the Russian reforms and accusations against the reformers