Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-15T15:28:04.806Z Has data issue: false hasContentIssue false
  • English
  • Français

On the Limits of Glm for SpecificationTesting: A Comment on Gurmu andTrivedi

Published online by Cambridge University Press:  11 February 2009

Jeffrey M. Wooldridge
Get accessRights & Permissions [Opens in a new window]

Abstract

In this comment on Gurmu and Trivedi's “VariableAugmentation Specification Tests in the LinearExponential Family,” I show how their generalizedlinear model (GLM) approach relates to other work ineconometrics on specification testing in the linearexponential family. In addition to shedding light onthe relationship between the statistics andeconometrics literatures on testing inquasi-likelihood frameworks, this comparison revealssome important limitations of GLM as a generalframework for devising specification tests.

Information

Type
Research Article
Information
Econometric Theory , Volume 10 , Issue 2 , June 1994, pp. 409 - 418
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1. Davidson, R. & MacKinnon, J.G.. Convenient specification tests for logit and probit models. Journal of Econometrics 24 (1984): 241–262.10.1016/0304-4076(84)90001-0CrossRefGoogle Scholar
2. Efron, B. Double exponential families and their use in generalized linear regression. Journal of the American Statistical Association 81 (1986): 709–721.10.1080/01621459.1986.10478327CrossRefGoogle Scholar
3.Engle, R.F. Wald, likelihood ratio, and Lagrange multiplier tests in econometrics. In Griliches, Z. & Intriligator, M.D. (eds.), Handbook of Econometrics, Volume II, pp. 775–826 Amsterdam: Elsevier, 1984.10.1016/S1573-4412(84)02005-5CrossRefGoogle Scholar
4. Gourieroux, C, Monfort, A. & Trognon, A.. Pseudo-maximum likelihood methods: Theory. Econometrica 52 (1984): 701–720.10.2307/1913472CrossRefGoogle Scholar
5. Gurmu, S. & Trivedi, P.K.. Variable augmentation specification tests in the exponential family. Econometric Theory 9 (1993): 94–113.10.1017/S0266466600007350CrossRefGoogle Scholar
6. McCullagh, P. & Nelder, J.A.. Generalized linear models. New York: Chapman and Hall, 2nd ed., 1989.10.1007/978-1-4899-3242-6CrossRefGoogle Scholar
7. Morton, R. A generalized linear model with nested strata of extra-Poisson variation. Biometrika 74 (1987): 247–257.10.1093/biomet/74.2.247CrossRefGoogle Scholar
8. Nelder, J.A. & Wedderburn, R.W.M.. Generalized linear models. Journal of the Royal Statistical Society A 135 (1972): 370–384.10.2307/2344614CrossRefGoogle Scholar
9. Wedderburn, R.W.M. Quasi-likelihood functions, generalized linear models and the Gauss-Newton method. Biometrika 61 (1974): 439–447.Google Scholar
10. Wooldridge, J.M. On the application of robust, regression-based diagnostics to models of conditional means and conditional variances. Journal of Econometrics 47 (1991): 5–46.10.1016/0304-4076(91)90076-PCrossRefGoogle Scholar
11. Wooldridge, J.M. Specification testing and quasi-maximum likelihood estimation. Journal of Econometrics 48 (1991): 29–55.10.1016/0304-4076(91)90031-8CrossRefGoogle Scholar