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Abstract Details
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Activity Number:
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519
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #301889 |
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Title:
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Should We Always Use the Logarithmic Transformation for Positive Response Variables?
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Author(s):
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Shengwu Shang*+ and Jeffrey M. Wooldridge
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Companies:
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Michigan State University and Michigan State University
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Address:
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Department of Economics, East Lansing, MI, 48824-1038,
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Keywords:
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LFE; ;
PQML; ;
GMM; ;
Consistency ;
Efficiency;
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Abstract:
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We compare three main estimation methods for positive response variable-- FE method for log linear model (LFE), Poisson Quasi-Maximum Likelihood (PQML) and Generalized Method of Moment (GMM) -- by Mont Carlo Simulation and real life data set. It is not surprising that LFE estimator is not consistent when PQML is; however, we do find circumstance where both LFE and PQML estimators are consistent plus LFE is more efficient. With this regard, we introduce GMM to improve the efficiency of PQML estimator as well as keeping the consistency; this way also finds a solution to the problem raised in Wooldridge (1999). From the simulation results, we find that GMM can reduce the standard errors of PQML estimators by almost a half. We also apply the GMM to a real life data set and the result shows that GMM is applicable.
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