Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold

Bibliographic Data

ID19418342
AuthorsXinyu Zhang (0000-0002-4426-939X, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China), Alan T K Wan (0000-0002-7205-4070, City University of Hong Kong), Sherry Zhou (0000-0003-4956-3843, City University of Hong Kong), Sherry Z Zhou (City University of Hong Kong)
Year2012
Volume30
Issue1
Pages132-142
Publication date2012-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1198/jbes.2011.10075
OpenAlexW2070674494
LanguageEN
Citations received4
References cited41

Claeskens and Hjort (2003) have developed a focused information criterion (FIC) for model selection that selects different models based on different focused functions with those functions tailored to the parameters singled out for interest. Hjort and Claeskens (2003) also have presented model averaging as an alternative to model selection, and suggested a local misspecification framework for studying the limiting distributions and asymptotic risk properties of post-model selection and model average estimators in parametric models. Despite the burgeoning literature on Tobit models, little work has been done on model selection explicitly in the Tobit context. In this article we propose FICs for variable selection allowing for such measures as mean absolute deviation, mean squared error, and expected expected linear exponential errors in a type I Tobit model with an unknown threshold. We also develop a model average Tobit estimator using values of a smoothed version of the FIC as weights. We study the finite-sample performance of model selection and model average estimators resulting from various FICs via a Monte Carlo experiment, and demonstrate the possibility of using a model screening procedure before combining the models. Finally, we present an example from a well-known study on married women's working hours to illustrate the estimation methods discussed. This article has supplementary material online

Econometrics · Estimator · Mean squared error · Model selection · Monte Carlo method · Parametric statistics · Statistics · Tobit model · Computer Science · Insurance, Mortality, Demography, Risk Management · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Artificial Intelligence

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