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Estimating Error Component Models WithGeneral MA(q)Disturbances

Published online by Cambridge University Press:  11 February 2009

Badi H. Baltagi  and
Qi Li
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Abstract

This paper provides a simple estimation method for anerror component regression model with generalMA(q) remainder disturbances. Theestimation method utilizes the transformationderived by Baltagi and Li [3] for an error componentmodel with autoregressive remainder disturbances,and a standard orthogonalizing algorithm for thegeneral MA(q) model. Thisestimation method is computationally simpleutilizing only least-squares regressions. This isimportant for panel data regressions where bruteforce GLS is in many cases not feasible.Thisestimation method performs well relative to true GLSin Monte-Carlo experiments.

Information

Type
Brief Report
Information
Econometric Theory , Volume 10 , Issue 2 , June 1994 , pp. 396 - 408
Copyright
Copyright © Cambridge University Press 1994

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References

1. Ansley, C.F. An algorithm for the exact likelihood of a mixed autoregressive-moving average process. Biometrika 66 (1979): 59–65.10.1093/biomet/66.1.59CrossRefGoogle Scholar
2. Balestra, P. A., note on the exact transformation associated with the first-order moving average process. Journal of Econometrics 14 (1980): 381–394.10.1016/0304-4076(80)90034-2Google Scholar
3. Baltagi, B.H. & Li, Q.. A transformation that will circumvent the problem of autocorrelation in an error component model. Journal of Econometrics 48 (1991): 385–393.10.1016/0304-4076(91)90070-TCrossRefGoogle Scholar
4. Brockwell, P. J. & Davis, R.A.. Time series: Theory and Methods. New York: Springer-Verlag, 1991.10.1007/978-1-4419-0320-4CrossRefGoogle Scholar
5. Fuller, W.A. & Battese, G.E.. Estimation of linear models with cross-error structure. Journal of Econometrics 2 (1974): 67–78.10.1016/0304-4076(74)90030-XCrossRefGoogle Scholar
6. Galbraith, J.W. & Zinde-Walsh, V.. The GLS transformation matrix and a semi-recursive estimator for the linear regression model with ARMA errors. Econometric Theory 8 (1992a): 95–111.10.1017/S0266466600010756CrossRefGoogle Scholar
7. Galbraith, J.W. & Zinde-Walsh, V.. Transforming the error-component model for estimation with general ARMA disturbances. Unpublished paper, McGill University, Montreal, Quebec, Canada, 1992b.Google Scholar
8. Hannan, E.J. & Rissanen, J.. Recursive estimation of ARMA order. Biometrika 69 (1982): 81–94.10.1093/biomet/69.1.81CrossRefGoogle Scholar
9. Hsiao, C. Analysis of Panel Data. Cambridge: Cambridge University Press, 1986.Google Scholar
10. Lee, Lung-Fei. Estimation of autocorrelated error components model with panel data. Working Paper, Department of Economics, University of Minnesota, 1979.Google Scholar
11. Li, Q. Estimating a linear regression model with general MA(q) disturbances. Working Paper, University of Guelph, Ontario, Canada, 1992.Google Scholar
12. Lillard, L.A. & Willis, R. J.. Dynamic aspects of earning mobility. Econometrica 46 (1978): 985–1012.10.2307/1911432CrossRefGoogle Scholar
13. MaCurdy, T.A. The use of time series processes to model the error structure of earnings in a longitudinal data analysis. Journal of Econometrics 18 (1982): 83–114.10.1016/0304-4076(82)90096-3CrossRefGoogle Scholar
14. MacDonald, G.M. & MacKinnon, J.G.. Convenient methods for estimation of linear regression models with MA(1) errors. Canadian Journal of Economics 18 (1985): 106–116.10.2307/135116CrossRefGoogle Scholar
15. Magnus, J.R. & Woodland, A.D.. On the maximum likelihood estimation of multivariate regression models containing serially correlated error components. International Economic Review 29 (1988): 707–725.10.2307/2526829Google Scholar
16. Nerlove, M. Further evidence on the estimation of dynamic economic relations from a time-series of cross sections. Econometrica 39 (1971): 359–382.10.2307/1913350CrossRefGoogle Scholar
17. Nicholls, D.F., Pagan, A.R. & Terrell, R.D.. The estimation and use of models with moving average disturbance terms: a survey. International Economic Review 16 (1975): 113–134.10.2307/2525888CrossRefGoogle Scholar
18. Pesaran, M.H. Exact maximum likelihood estimation of a regression equation with a first-order moving-average error. Review of Economic Studies 40 (1973): 529–536.10.2307/2296586CrossRefGoogle Scholar
19. Phillips, P.C.B. & Ploberger, W.. Time series modeling with a Bayesian frame of reference: concepts, illustrations and asymptotics. Cowles Foundation Discussion Paper No. 1038, 1992a.Google Scholar
20. Phillips, P.C.B. & Ploberger, W.. Posterior odds testing for a unit root with data-based model selection. Cowles Foundation Discussion Paper No. 1017, 1992b.Google Scholar
21. Revankar, N.S. Error component models with serially correlated time effects. Journal of the Indian Statistical Association 17 (1979): 137–160.Google Scholar
22. Schwarz, G. Estimating the dimension of a model. Annals of Statistics 6 (1978): 461–464.10.1214/aos/1176344136CrossRefGoogle Scholar
23. Ullah, A., Vinod, H.D. & Singh, R.S.. Estimation of linear models with moving average disturbances. Journal of Quantitative Economics 2 (1986): 137–152.Google Scholar
24. Wansbeek, T. & Kapteyn, A.. A simple way to obtain the spectral decomposition of variance components models for balanced data. Communications in Statistics All (1982): 2105–2112.10.1080/03610928208828373Google Scholar
25. Wansbeek, T. & Kapteyn, A. A note on spectral decomposition and maximum likelihood estimation of ANOVA models with balanced data. Statistics and Probability Letters 1 (1983): 213–215.10.1016/0167-7152(83)90033-0CrossRefGoogle Scholar