The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification

Chuhui Li, Donald Poskitt and Xueyan Zhao

Journal of Econometrics, 2019, vol. 209, issue 1, 94-113

Abstract: This paper examines the notion of “identification by functional form” for two equation triangular systems for binary endogenous variables by providing a bridge between the literature on the recursive bivariate probit model and that on partial identification. We evaluate the impact of functional form on the performance of (quasi) maximum likelihood estimators, and investigate the practical importance of available instruments in both cases of correct and incorrect distributional specification. Finally, we calculate average treatment effect bounds and demonstrate how properties of the estimators are explicable via a link between the notion of pseudo-true parameters and the concepts of partial identification.

Keywords: Average treatment effect; Binary outcome models; Copula; Identified set; Instrumental variables; Misspecification (search for similar items in EconPapers)
JEL-codes: C31 C35 C36 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

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Related works:
Working Paper: The Bivariate Probit Model, Maximum Likelihood Estimation, Pseudo True Parameters and Partial Identification (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:209:y:2019:i:1:p:94-113

DOI: 10.1016/j.jeconom.2018.07.009

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