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Estimation of Mixture Models using Cross‐Validation Optimization: Implications for Crop Yield Distribution Modeling
Author(s) -
Woodard Joshua D.,
Sherrick Bruce J.
Publication year - 2011
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1093/ajae/aar034
Subject(s) - econometrics , parametric statistics , nonparametric statistics , sample size determination , parametric model , sample (material) , model selection , selection (genetic algorithm) , yield (engineering) , cross validation , statistics , computer science , mathematics , machine learning , chromatography , metallurgy , chemistry , materials science
A critical issue in identifying an appropriate characterization of crop yield distributions is that the best‐fitting distribution in an in‐sample framework is not necessarily the best choice out‐of‐sample. This study provides a methodology for estimating flexible and efficient mixture models using cross‐validation that alleviates many of these associated model selection issues. The method is illustrated in an application to the rating of group risk insurance products. Results indicate that nonparametric models often fit best in‐sample but are inefficient and consistently overstate true rates, and vice versa for parametric models. The proposed model provides unbiased rates and also has desirable efficiency properties.