Dual Generalized Maximum Entropy Estimation for Panel Data Regression ModelsDual Generalized Maximum Entropy Estimation \\ for Panel Data Regression Models
- Other Titles
- Dual Generalized Maximum Entropy Estimation \\ for Panel Data Regression Models
- Authors
- 이재준; 전수영
- Issue Date
- 2014
- Publisher
- 한국통계학회
- Keywords
- Collinearity; endogeneity; exogeneity; generalized maximum entropy; ill-posed problems; panel data
- Citation
- Communications for Statistical Applications and Methods, v.21, no.5, pp.395 - 409
- Indexed
- KCI
- Journal Title
- Communications for Statistical Applications and Methods
- Volume
- 21
- Number
- 5
- Start Page
- 395
- End Page
- 409
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/100667
- ISSN
- 2287-7843
- 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.
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Collections - Graduate School > Department of Applied Statistics > 1. Journal Articles
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