A composite generalized cross-entropy formulation in small samples estimation

Econometric Reviews

Volumn 27, Issue 4-6, 2008, Pages 596-609

  Papalia, R Bernardini ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

Generalized cross entropy; Microeconometric models; Repeated samples; Small sample estimation

Indexed keywords

EID: 45849147015     PISSN: 07474938     EISSN: 15324168     Source Type: Journal    
DOI: 10.1080/07474930801960469     Document Type: Article

References (18)

1. Amemiya, T. (1984). Tobit models: A survey. Journal of Econometrics 24:3-61.
2. Bernardini Papalia, R. (2004). Estimating item weights of consumer price indexes for small reference populations. In: Proceedings of the European Conference on Quality and Methodology in Official Statistics (Q2004). Mainz.
3. Bernardini Papalia, R., Filippucci, C. (2000). Inference from non-random samples: a maximum entropy approach. In: Proceedings of the Second International Conference on Establishment Surveys. Buffalo, New York, pp. 1686-1691.
4. Deaton, A. S., Muellbauer, J. (1980). Economics and Consumer Behavior. Cambridge: Cambridge University Press.
5. Golan, A. (2001). A simultaneous estimation variable selection rule. Journal of Econometrics 10:165-193.
6. Golan, A., Judge, G., Miller, D. (1996a). Maximum Entropy Econometrics: Robust Estimation with Limited Data. New York: Wiley.
7. Golan, A., Judge, G., Perloff, J. (1996b). A maximum entropy approach to recovering information from multinomial response data. Journal of the American Statistical Association 91:841-853.
8. Golan, A., Judge, G., Miller, D. (1997a). The maximum entropy approach to estimation and inference: An overview. In: Fomby, T. B., Hill, R. C., eds. Advances in Econometrics. Applying Maximum Entropy to Econometric Problems. Vol. 12. London, UK: Jai Press Ltd., pp. 3-24.
9. Golan, A., Judge, G., Perloff, J. (1997b). Estimation and inference with censored and ordered multinomial response data. Journal of Econometrics 79:23-51.
10. Golan, A., Judge, G., Miller, D. (1998). Information recovery in simultaneous-equations statistical models. In: Ullah, A., Giles, D., eds. Handbook of Applied Economic Statistics. New York: Marcel Dekker, pp. 365-381.
11. Golan, A., Perloff, J., Shen, E. (2001). Estimating a demand system with nonnegativity constraints: Mexican meat demand. The Review of Economics and Statistics 83(3):541-550.
12. Jaynes, E. T. (1957). Information theory and statistical mechanics. Physics Review 106:620-630.
13. Kullback, J. (1959). Information Theory and Statistics. New York, NY: Wiley.
14. Levine, R. D. (1980). An information theoretical approach to inversion problems. Journal of Physics A 13:91-108.
15. Mittelhammer, R., Cardell, S. (1996). On the consistency and asymptotic normality of the data constrained GME estimator of the GML. Working paper, Washington State University, Pullman, WA.
16. Owen, A. B. (1991). Empirical likelihood for linear models. The Annals of Statistic 19:1725-1747.
17. Qin, J., Lawless, J. (1994). Empirical likelihood and general estimating equations. The Annals of Statistics 22:300-325.
18. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal 27:379-423.