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http://dx.doi.org/10.5351/CSAM.2014.21.5.395

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)
Publication Information
Communications for Statistical Applications and Methods / v.21, no.5, 2014 , pp. 395-409 More about this Journal
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;
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Times Cited By KSCI : 1  (Citation Analysis)
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