Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-05T11:42:10.136Z Has data issue: false hasContentIssue false
  • English
  • Français

A General Approach to Serial Correlation

Published online by Cambridge University Press:  18 October 2010

C. Gourieroux ,
A. Monfort  and
A. Trognon
C. Gourieroux
Affiliation:
Université de Lille et CEPREMAP, Panis
A. Monfort
Affiliation:
Unité de Recherche, INSEE, Paris
A. Trognon
Affiliation:
ENSAE, Paris
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper the testing and estimation problems are discussed in the case of serial correlation. Various models are particular cases of the general framework considered: the nonlinear simultaneous equations models, the probit models, the tobit models, the disequilibrium models, the frontier models, etc. In this context, it is shown that the score test can be written explicitly and that the statistic obtained is a generalization of that of Durbin and Watson; moreover, the maximum likelihood estimation procedure is shown to be robust with respect to serial correlation.

Type
Research Article
Information
Econometric Theory , Volume 1 , Issue 3 , December 1985 , pp. 315 - 340
Copyright
Copyright © Cambridge University Press 1985 

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.)

References

REFERENCES

1. Artus, P., Laroque, G., Michel, G.. Estimation of a quarterly macroeconomic model with quantity rationing. Econometrica 52 (1984): 387–414.Google Scholar
2. Burguete, J., Gallant, R., Souza, G.. On unification of the asymptotic theory of nonlinear econometrics. Econometric Reviews 1 (1982): 151–190.10.1080/07311768208800012Google Scholar
3. Chesher, A, Irish, M.. Residuals and diagnostics for probit, tobit and related models. University of Bristol Discussion Paper 84 /152 (1984).Google Scholar
4. Dagenais, M. The tobit model with serial correlation. Economics Letters 10 (1982): 263–267.Google Scholar
5. Durbin, J. Watson, G.. Testing for serial correlation in least squares regression. Biometrika 38 (1951): 159–178.Google Scholar
6. Godfrey, L. G. Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica 46 (1978): 1293–1302.10.2307/1913829Google Scholar
7. Godfrey, L. G. On the invariance of the Lagrange multiplier test with respect to certain changes in the alternative hypothesis, Econometrica 49 (1981): 1443–1455.Google Scholar
8. Godfrey, L. G. Wickens, M. R.. Tests of misspecification using locally equivalent alternative models. In Evaluating the Reliability of Macroeconomic Models. New York; Wiley, 1982.Google Scholar
9. Gourieroux, C., Laffont, J. J. and Monfort, A.. Coherency conditions in simultaneous linear equation models with endogenous switching regimes. Econometrica 48 (1980): 675–695.Google Scholar
10. Gourieroux, C., Monfort, A., and Trognon, A.. Estimation and test in probit models with serial correlation. In Alternative Approaches to Time Series Analysis. Publications des Facultes Universitaires Saint-Louis: Brussels, 1984.Google Scholar
11. Gourieroux, C., Monfort, A., Renault, E., and Trognon, A.. Résidus généralisés ou interprétations linéaires de l' économétrie non linéaire. Document de travail ENSAE-INSEE no. 8410 (1984).Google Scholar
12.Mac Rae Kennan, D. A time series analysis of binary data. Journal of the American Statistical Association 77 (1982): 816–821.Google Scholar
13. Pearson, K. On the correlation of characters not quantitatively measurable. Philosophical Transactions of the Royal Society 195 (1901).Google Scholar
14. Robinson, P. On the asymptotic properties of estimators of models containing limited dependent variables. Econometrica 50 (1982): 27–41.Google Scholar
15. Slepian, D. The one sided barrier problem for gaussian noise. Bell System Technical Journal 41 (1962): 463–501.10.1002/j.1538-7305.1962.tb02419.xGoogle Scholar
16. White, H. Maximum likelihood of misspecified models Econometrica 50 (1982): 1–26 Google Scholar