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Partially adaptive estimation via the maximum entropy densities

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  • Ximing Wu
  • Thanasis Stengos

Abstract

We propose a partially adaptive estimator based on information theoretic maximum entropy estimates of the error distribution. The maximum entropy (maxent) densities have simple yet flexible functional forms to nest most of the mathematical distributions. Unlike the non-parametric fully adaptive estimators, our parametric estimators do not involve choosing a bandwidth or trimming, and only require estimating a small number of nuisance parameters, which is desirable when the sample size is small. Monte Carlo simulations suggest that the proposed estimators fare well with non-normal error distributions. When the errors are normal, the efficiency loss due to redundant nuisance parameters is negligible as the proposed error densities nest the normal. The proposed partially adaptive estimator compares favourably with existing methods, especially when the sample size is small. We apply the estimator to a stochastic frontier model, whose error distribution is usually non-normal. Copyright 2005 Royal Economic Society

Suggested Citation

  • Ximing Wu & Thanasis Stengos, 2005. "Partially adaptive estimation via the maximum entropy densities," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 352-366, December.
    • Handle: RePEc:ect:emjrnl:v:8:y:2005:i:3:p:352-366
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    References listed on IDEAS

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    Cited by:

    1. Theodore Panagiotidis & Georgios Papapanagiotou, 2024. "A note on the determinants of NFTs returns," Discussion Paper Series 2024_02, Department of Economics, University of Macedonia, revised Feb 2024.
    2. Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
    3. Rompolis, Leonidas S., 2010. "Retrieving risk neutral densities from European option prices based on the principle of maximum entropy," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 918-937, December.
    4. Steven Caudill & James Long, 2010. "Do former athletes make better managers? Evidence from a partially adaptive grouped-data regression model," Empirical Economics, Springer, vol. 39(1), pages 275-290, August.
    5. Wu, Ximing & Perloff, Jeffrey M., 2005. "GMM Estimation of a Maximum Distribution With Interval Data," Institute for Research on Labor and Employment, Working Paper Series qt7jf5w1ht, Institute of Industrial Relations, UC Berkeley.
    6. Steven Caudill, 2012. "A partially adaptive estimator for the censored regression model based on a mixture of normal distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 121-137, June.
    7. Thanasis Stengos & Ximing Wu, 2010. "Information-Theoretic Distribution Test with Application to Normality," Econometric Reviews, Taylor & Francis Journals, vol. 29(3), pages 307-329.
    8. Katherine G. Yewell & Steven B. Caudill & Franklin G. Mixon, Jr., 2014. "Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach," Econometrics, MDPI, vol. 2(1), pages 1-19, February.
    9. Emanuele Taufer & Sudip Bose & Aldo Tagliani, 2009. "Optimal predictive densities and fractional moments," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(1), pages 57-71, January.
    10. Wu, Ximing & Perloff, Jeffrey M., 2005. "GMM Estimation of a Maximum Distribution With Interval Data," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt7jf5w1ht, Department of Agricultural & Resource Economics, UC Berkeley.
    11. Sella Lisa, 2008. "Old and New Spectral Techniques for Economic Time Series," Department of Economics and Statistics Cognetti de Martiis. Working Papers 200809, University of Turin.
    12. Pendharkar, Parag C., 2008. "Maximum entropy and least square error minimizing procedures for estimating missing conditional probabilities in Bayesian networks," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3583-3602, March.

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