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A Study of Generalized Maximum Entropy Estimator for the Panel Regression Model

패널회귀모형에서 최대엔트로피 추정량에 관한 연구

  • Published : 2006.11.30

Abstract

This paper considers a panel regression model with ill-posed data and proposes the generalized maximum entropy(GME) estimator of the unknown parameters. These are natural extensions from the biometries, statistics and econometrics literature. The performance of this estimator is investigated by using of Monte Carlo experiments. The results indicate that the GME method performs the best in estimating the unknown parameters.

횡단면 자료와 시계열 자료가 병합된 패널회귀모형을 다루는 대부분의 연구들에서 사용되고 있는 자료는 완전한 자료를 고려하고 있다. 그러나, 실제적으로 완전한 자료보다는 불완전한 자료가 많다. 이러한 상황을 고려하지 않고 통계적인 추론을 하게 되면 잘못된 결론이 도출될 수 있다. 따라서, 자료의 형태를 충분히 고려한 추정량을 바탕으로 자료를 분석해야 한다. 본 연구는 패널회귀모형에서 자료가 불완전 상태인 경우 최대 엔트로피 형식을 이용한 일반화최대엔트로피 추정량을 제안하고, 추정량들의 효율성을 모의실험을 통하여 비교하였다. 모의실험 결과, 일반화 최대엔트로피 추정량이 가장 안정적이고 효율적인 추정량임을 보여주었다.

Keywords

References

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