Dual Generalized Maximum Entropy Estimation for Panel Data Regression Models

- Journal title : Communications for Statistical Applications and Methods
- Volume 21, Issue 5, 2014, pp.395-409
- Publisher : The Korean Statistical Society
- DOI : 10.5351/CSAM.2014.21.5.395

Title & Authors

Dual Generalized Maximum Entropy Estimation for Panel Data Regression Models

Lee, Jaejun; Cheon, Sooyoung;

Lee, Jaejun; Cheon, Sooyoung;

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

2.

중도절단 패널회귀모형에서의 이중 일반화 최대엔트로피 추정,이재준;이동희;전수영;

Journal of the Korean Data Analysis Society, 2016. vol.18. 3B, pp.1271-1280

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