Browse > Article
http://dx.doi.org/10.5351/KJAS.2006.19.3.521

A Study of Generalized Maximum Entropy Estimator for the Panel Regression Model  

Song, Seuck-Heun (Dept. of Statistics, Korea University)
Cheon, Soo-Young (Dept. of Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.19, no.3, 2006 , pp. 521-534 More about this Journal
Abstract
This paper considers a panel regression model with ill-posed data and proposes the generalized maximum entropy(GME) estimator of the unknown parameters. These are natural extensions from the biometries, statistics and econometrics literature. The performance of this estimator is investigated by using of Monte Carlo experiments. The results indicate that the GME method performs the best in estimating the unknown parameters.
Keywords
Panel Regression Model; Information Recovery; ME Estimation; GME Estimation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Shannon, C. E. (1948). A mathematical theory of communication, Bell system Technical Journal, 27, 379-423   DOI
2 Golan, A. (1994). A multi-variable stochastic theory of size distribution of firms with empirical evidence, Advances in Econometrics, 10, 1-46
3 Wansbeek, T. J. and Kapteyn, A. (1982). A simple way to obtain the spectral decomposition of variance components model for balanced data, Communication in Statistics, 11, 2105-2112   DOI
4 Levine, R. D. and Tribus, M. (1979). The Maximum Entropy Formalism, MIT Press, Cambridge
5 Golan, A., Judge, G. and Robinson, S. (1994). Recovering information in the case of partial multisectorial economic data, Review of Economics and Statistics, 76, 541-549   DOI   ScienceOn
6 Golan, A. and Judge, G. (1996). Recovering information in the case of underdetermined problems and incomplete data, Journal of Statistical Planning and Inference, 49, 127-136   DOI   ScienceOn
7 Baltagi, B. H., Song, S. H. and Jung, B. C. (2002). Simple LM tests for unbalanced nested error component regression model, Econometric Reviews, 21, 167-187   DOI   ScienceOn
8 Baltagi, B. H., Song, S. H. and Koh, W. (2003). Testing panel data regression model with spatial error correlation, Journal of Econometrics, 117, 123-150   DOI   ScienceOn
9 Baltagi, B. H., Song, S. H. and Jung, B. C. (2001). The unbalanced nested error component regression model, Journal of Econometrics, 101, 357-381   DOI   ScienceOn
10 Belsley, D. (1991). Conditioning Diagnostics: Collinearity and Weak Data in Regression, John Wiley, New York
11 Golan, A., Judge, G., and Miller, D. (1996). Maximum Entropy Econometrics: Robust Estimation with Limited Data, John Wiley, New York
12 Judge, G. G. and Golan, A. (1992). Recovering information in the case of ill-posed inverse problems with noise, Unpublished paper, University of California at Berkeley
13 Jaynes, E. T. (1957a). Information theory and statistical mechanics, Physics Review, 106, 620-30   DOI
14 Jaynes, E. T. (1957b). Information theory and statistical mechanics II, Physics Review, 108, 171-90   DOI
15 Jaynes, E. T. (1984). Prior information and ambiguity in inverse problems, In D. W. McLaughlin (Ed.) Inverse problems, p.151-66, SIAM Proceedings, American Mathematical Society, Providence, RI
16 Judge, G. G., Hill, R. C., Griffiths, W. E., Lutkepohl, H. and Lee, T. C. (1988). Introduction to the Theory and Practice of Econometrics, John Wiley, New York
17 Levine, R. D. (1980). An information theoretical approach to inversion problems, Journal of Physics, 13, 91-108
18 Moulton, B. R. (1986). Random group effects and precision of regression estimates, Journal of Econometrics, 32, 385-397   DOI   ScienceOn
19 Swamy, P. A. V. B. and Arora, S. S. (1972). The exact finite sample properties of the estimators of coefficients in the error components regression models, Econometrica, 40, 261-275   DOI   ScienceOn
20 Amemiya, T. (1971). The estimation of the variances in a variance components model, International Econometric Review, 12, 1-13   DOI   ScienceOn