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Estimation of sample selection models with spatial dependence
Author(s) -
FloresLagunes Alfonso,
Schnier Kurt Erik
Publication year - 2012
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.1189
Subject(s) - estimator , autoregressive model , selection (genetic algorithm) , model selection , econometrics , statistics , sample (material) , probit model , generalized method of moments , sample size determination , spatial dependence , estimation , computer science , mathematics , selection bias , economics , artificial intelligence , chromatography , management , chemistry
SUMMARY We consider the estimation of a sample selection model that exhibits spatial autoregressive errors (SAE). Our methodology is motivated by a two‐step strategy where in the first step we estimate a spatial probit model and in the second step (outcome equation) we include an estimated inverse Mills ratio (IMR) as a regressor to control for selection bias. Since the appropriate IMR under SAE depends on a parameter from the second step, both steps are jointly estimated employing the generalized method of moments. We explore the finite sample properties of the estimator using simulations and provide an empirical illustration. Copyright © 2010 John Wiley & Sons, Ltd.