Communications for Statistical Applications and Methods

  • 제21권5호
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  • Pages.395-409
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  • 2014
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Dual Generalized Maximum Entropy Estimation for Panel Data Regression Models

  • Lee, Jaejun (Center for Military Planning, Korea Institute for Defense Analyses) ;
  • Cheon, Sooyoung (Department of Applied Statistics, Korea University)
  • 투고 : 2014.05.26
  • 심사 : 2014.08.23
  • 발행 : 2014.09.30
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초록

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.

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참고문헌

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피인용 문헌

  1. Estimation for the multi-way error components model with ill-conditioned panel data vol.46, pp.1, 2017, https://doi.org/10.1016/j.jkss.2016.05.008
  2. Comparison of entropy measures in generalized maximum entropy estimation vol.1053, pp.1742-6596, 2018, https://doi.org/10.1088/1742-6596/1053/1/012021