EFFICIENT ESTIMATION AND MODEL SELECTION FOR GROUPED DATA WITH LOCAL MOMENTS

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概要

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• This paper proposes efficient estimation methods of unknown parameters when frequencies as well as local moments are available in grouped data. Assuming the original data is an i.i.d. sample from a parametric density with unknown parameters, we obtain the joint density of frequencies and local moments, and propose a maximum likelihood (ML) estimator. We further compare it with the generalized method of moments (GMM) estimator and prove these two estimators are asymptotically equivalent in the first order. Based on the ML method, we propose to use the Akaike information criterion (AIC) for model selection. Monte Carlo experiments show that the estimators perform remarkably well, and AIC selects the right model with high frequency.

著者

• Liu Qing-feng

  Kyoto University

• Hitomi Kohtaro

  Kyoto Institute of Technology

• Nishiyama Yoshihiko

  Kyoto University

• Sueishi Naoya

  University of Wisconsin-Madison

関連論文

• EFFICIENT ESTIMATION AND MODEL SELECTION FOR GROUPED DATA WITH LOCAL MOMENTS
• Efficiency Improvement by Local Moments in Grouped Data Analysis
• EFFICIENT ESTIMATION AND MODEL SELECTION FOR GROUPED DATA WITH LOCAL MOMENTS
• EFFICIENT ESTIMATION AND MODEL SELECTION FOR GROUPED DATA WITH LOCAL MOMENTS

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