The Information Matrix Test for the Linear Model
AbstractWe derive the information matrix test, suggested by White (1982), for the normal fixed regressor linear model, and show that the statistic decomposes asymptotically into the sum of three independent quadratic forms. One of these is White's (1980) general test for heteroscedasticity and the remaining two components are quadratic forms in the third and forth powers of the residuals respectively. Our results show that the test will fail to detect serial correlation and never be asympotically optimal against heteroscedasticity, skewness and non-normal kurtosis. The information matrix test is contrasted with the test procedures of Bera and Jarque (1983) and Godfrey and Wickens (1982), who construct a composite statistic form asymptotically optimal and independent tests against particular alternatives. Our results suggest that this alternative strategy is likely to be a more fruitful source of a general regression diagnostic.
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Bibliographic InfoPaper provided by University of Warwick, Department of Economics in its series The Warwick Economics Research Paper Series (TWERPS) with number 250.
Length: 29 pages
Date of creation: 1984
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- Riccardo LUCCHETTI & Claudia PIGINI, 2011. "Conditional Moment Tests for Normality in Bivariate Limited Dependent Variable Models: a Monte Carlo Study," Working Papers 357, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
- Joachim Zietz & Bobby Newsome, 2001. "A Note on Buyer's Agent Commision and Sales Price," Journal of Real Estate Research, American Real Estate Society, vol. 21(3), pages 245-254.
- Russell Davidson & James G. MacKinnon, 1987.
"Testing for Consistency using Artificial Regressions,"
687, Queen's University, Department of Economics.
- Davidson, Russell & MacKinnon, James G., 1989. "Testing for Consistency using Artificial Regressions," Econometric Theory, Cambridge University Press, vol. 5(03), pages 363-384, December.
- Geert Dhaene & Dirk Hoorelbeke, 2002.
"The Information Matrix Test with Bootstrap-Based Covariance Matrix Estimation,"
Center for Economic Studies - Discussion papers
ces0211, Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
- Dhaene, Geert & Hoorelbeke, Dirk, 2004. "The information matrix test with bootstrap-based covariance matrix estimation," Economics Letters, Elsevier, vol. 82(3), pages 341-347, March.
- Dirk Hoorelbeke, 2004. "Bootstrap correcting the score test," Econometric Society 2004 North American Summer Meetings 228, Econometric Society.
- Joachim Zietz, 2005.
"Detecting Neglected Parameter Heterogeneity with Chow Tests,"
200503, Middle Tennessee State University, Department of Economics and Finance.
- Joachim Zietz, 2006. "Detecting neglected parameter heterogeneity with Chow tests," Applied Economics Letters, Taylor & Francis Journals, vol. 13(6), pages 369-374.
- Riccardo Lucchetti & Claudia Pigini, 2013. "A test for bivariate normality with applications in microeconometric models," Statistical Methods and Applications, Springer, vol. 22(4), pages 535-572, November.
- Russell Davidson & James G. MacKinnon, 1994.
"Graphical Methods for Investigating the Size and Power of Hypothesis Tests,"
903, Queen's University, Department of Economics.
- Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
- Calzolari, Giorgio & Panattoni, Lorenzo, 1984. "A Simulation Study on FIML Covariance Matrix," MPRA Paper 28804, University Library of Munich, Germany.
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