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A simple, flexible estimator for count and other ordered discrete data
Author(s) -
Mroz Thomas A.
Publication year - 2010
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1211
Subject(s) - estimator , poisson distribution , covariate , econometrics , count data , computer science , monte carlo method , hazard , simple (philosophy) , distribution (mathematics) , statistics , mathematics , mathematical analysis , philosophy , chemistry , organic chemistry , epistemology
SUMMARY This paper examines a flexible way to model empirically discrete data outcomes using ‘hazard rate’ decompositions. It presents a general data‐generating mechanism based on potential outcomes to describe why the approach should work for almost any discrete distribution. Monte Carlo evidence indicates that these models estimate well the impacts of covariates on expected counts when the data follow a Poisson distribution. With data from more complex processes, these estimators continue to perform well. Since most economic count outcomes arise from occurrence‐dependent behavioral processes, using flexibly estimated distributions should reduce the dependence of results on convenient but invalid assumptions. Copyright © 2010 John Wiley & Sons, Ltd.