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Dual Generalized Maximum Entropy Estimation for Panel Data Regression Models
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 Title & Authors
Dual Generalized Maximum Entropy Estimation for Panel Data Regression Models
Lee, Jaejun; Cheon, Sooyoung;
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 Abstract
Data limited, partial, or incomplete are known as an ill-posed problem. If the data with ill-posed problems are analyzed by traditional statistical methods, the results obviously are not reliable and lead to erroneous interpretations. To overcome these problems, we propose a dual generalized maximum entropy (dual GME) estimator for panel data regression models based on an unconstrained dual Lagrange multiplier method. Monte Carlo simulations for panel data regression models with exogeneity, endogeneity, or/and collinearity show that the dual GME estimator outperforms several other estimators such as using least squares and instruments even in small samples. We believe that our dual GME procedure developed for the panel data regression framework will be useful to analyze ill-posed and endogenous data sets.
 Keywords
Collinearity;endogeneity;exogeneity;generalized maximum entropy;ill-posed problems;panel data;
 Language
English
 Cited by
1.
불균형 패널회귀모형에서의 이중 일반화 최대엔트로피 추정,이재준;전수영;

Journal of the Korean Data Analysis Society, 2015. vol.17. 3B, pp.1285-1295
1.
Estimation for the multi-way error components model with ill-conditioned panel data, Journal of the Korean Statistical Society, 2016  crossref(new windwow)
 References
1.
Ahn, S. C. and Schmidt, P. (1999). Estimation of linear panel data models using GMM, in Generalized method of Moments Estimation, L. Matyas (Eds.), Cambridge University Press, Cambridge.

2.
Amemiya, T. (1971). The estimation of the variances in a variance-components models, International Economic Review, 12, 1-13. crossref(new window)

3.
Arora, S. (1973). Error components regression models and their applications, Annals of Economic and Social Measurement, 2, 451-462.

4.
Baltagi, B. H. (1981). Pooling: An experimental study of alternative testing and estimation procedures in a two-way error component model, Journal of Econometrics, 17, 21-49. crossref(new window)

5.
Baltagi, B. H. (2001). Econometric Analysis of Panel Data, John Wiley, New York.

6.
Belsley, D. A. (1991). Conditioning Diagnostics: Collinearity and Weak Data in Regression, John Wiley & Sons, New York.

7.
Dennis, J. E. and Schnabel, R. B. (1983). Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Englewood Cliffs, New Jersey.

8.
Everaert, G. and Pozzi, L. (2014). The predictability of aggregate consumption growth in OECD countries: A panel data analysis, Journal of Applied Econometrics, 29, 431-453. crossref(new window)

9.
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. crossref(new window)

10.
Golan, A., Judge, G. and Miller, D. (1996a). Maximum Entropy Econometrics: Robust Estimation with Limited Data, John Wiley & Sons, Chichester.

11.
Golan, A., Judge, G. and Perloff, J. (1996b). A maximum entropy approach to recovering information from multinomial response data, Journal of the American Statistical Association, 91, 841-853.

12.
Golan, A., Judge, G. and Perloff, J. (1997). Estimation and inference with censored and ordered multinomial response data, Journal of Econometrics, 79, 23-51. crossref(new window)

13.
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. crossref(new window)

14.
Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators, Econometrica, 50, 1029-1054. crossref(new window)

15.
Heckman, J. J. (1981). The incidental parameters problem and the problem of initial conditions in estimation a discrete time-discrete data stochastic process, in: C. Manski and D. McFadden, eds., Structural Analysis of Discrete Data with Econometric Application, MIT Press, Cambridge, Massachusetts.

16.
Hsiao, C. (1986). Analysis of Panel Data, Cambridge University Press, Cambridge.

17.
Im, K. S., Ahn, S. C., Schmidt, P. and Wooldridge, M. J. (1999). Efficient estimation of panel data models with strictly exogenous explanatory variables, Journal of Econometrics, 93, 177-201. crossref(new window)

18.
Jaynes, E. T. (1957a). Information theory and statistical mechanics I, Physics Review, 106, 620-630. crossref(new window)

19.
Jaynes, E. T. (1957b). Information theory and statistical mechanics II, Physics Review, 108, 171-190. crossref(new window)

20.
Jaynes, E. T. (1984). Prior information and ambiguity in inverse problems, In McLaughlin D.W. (Ed.) Inverse Problems, 151-166, SIAM Proceedings, American Mathematical Society, Providence, Rhode Island.

21.
Judge, G. and Golan, A. (1992). Recovering information in the case of ill-posed inverse problems with noise, University of California at Berkeley, Unpublished Paper.

22.
Kullback, J. (1959). Information Theory and Statistics, John Wiley, New York.

23.
Levine, R. D. (1980). An Information theoretical approach to inverse problems, Journal of Physics A, 13, 91-108. crossref(new window)

24.
Levine, R. D. and Tribus, M. (1979). The Maximum Entropy Formalism, MIT Press, Cambridge, MA.

25.
Nerlove, M. (1971). Further evidence on the estimation of dynamic economic relations from a time series of cross sections, Econometrica, 39, 359-382. crossref(new window)

26.
Schnabel, R. B., Koontz, J. E. and Weiss, B. E. (1985). A modular system of algorithms for uncon-strained minimization, ACM Transactions on Mathematical Software, 11, 419-440.

27.
Shannon, C. E. (1948). A mathematical theory of communication, Bell System Technical Journal, 27, 379-423. crossref(new window)

28.
Song, S. H. and Cheon, S. Y. (2006). A study of generalized maximum entropy estimator for the panel regression model, The Korean Journal of Applied Statistics, 19, 521-534. crossref(new window)

29.
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. crossref(new window)

30.
Wansbeek, T. and Kapteyn, A. (1989). Estimation of the error-components model with incomplete panels, Journal of Econometrics, 41, 341-361. crossref(new window)

31.
Wooldridge, J. M. (2003). Econometric Analysis of Cross Section and Panel Data, The MIT Press, Cambridge.