Estimating Causal Effects in Binary Response Models with Binary Endogenous Explanatory Variables - A Comparison of Possible Estimators

Manuel Denzer ()
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Manuel Denzer: Johannes Gutenberg University Mainz

No 1916, Working Papers from Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz

Abstract: This paper reviews and compares different estimators used in the past to estimate a binary response model (BRM) with a binary endogenous explanatory variable (EEV) to give practical insights to applied econometricians. It also gives a guidance how the average structural function (ASF) can be used in such a setting to estimate average partial effects (APEs). In total, the (relative) performance of six different linear parametric, non-linear parametric as well as non-linear semi-parametric estimators is compared in specific scenarios like the prevalence of weak instruments. A simulation study shows that the non-linear parametric estimator dominates in a majority of scenarios even when the corresponding parametric assumptions are not fullfilled. Moreover, while the semi-parametric non-linear estimator might be seen as a suitable alternative for estimating coefficients, it suffers from weaknesses in estimating partial effects. These insights are confirmed by an empirical illustration of the individual decision to supply labor.

Keywords: Binary choice; Binomial response; Binary Endogenous Explanatory Variable; Average Structural Function (search for similar items in EconPapers)
JEL-codes: C25 C26 (search for similar items in EconPapers)
Pages: 61 pages
Date: 2019-12-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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https://download.uni-mainz.de/RePEc/pdf/Discussion_Paper_1916.pdf First version, 2019 (application/pdf)

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