Testing exogeneity of multinomial regressors in count data models: does two stage residual inclusion work?

Andrea Geraci, Daniele Fabbri and Chiara Monfardini

Health, Econometrics and Data Group (HEDG) Working Papers from HEDG, c/o Department of Economics, University of York

Abstract: We study a simple exogeneity test in count data models with possibly endogenous multinomial treatment. The test is based on Two Stage Residual Inclusion 2SRI). Results from a broad Monte Carlo study provide novel evidence on important features of this approach in nonlinear settings. We find differences in the finite sample performance of various likelihood-based tests under correct specification and when the outcome equation is misspecified due to neglected over-dispersion or non-linearity. We compare alternative 2SRI procedures and uncover that standardizing the variance of the first stage residuals leads to higher power of the test and reduces the bias of the treatment coefficients. An original application in health economics corroborates our findings.

Keywords: count data; endogenous treatment; exogeneity test; health care utilization (search for similar items in EconPapers)
JEL-codes: C12 C31 C35 I11 (search for similar items in EconPapers)
Date: 2014-01
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Citations: View citations in EconPapers (14)

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Related works:
Journal Article: Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work? (2018)
Working Paper: Testing exogeneity of multinomial regressors in count data models: does two stage residual inclusion work? (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:yor:hectdg:14/03

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