arXiv 17 Aug 2018 · Econometrics · publishedJournal of Applied Econometrics (2019) · 32 citations (OpenAlex)
arXiv:1808.05792 · PDF · DOI · OpenAlex · Extracted main text
The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of the unobservables as a bivariate normal distribution, which results in a bivariate probit model. To address the problem of misspecification in this practice, we propose an easy-to-implement semiparametric estimation framework with parametric copula and nonparametric marginal distributions. We establish asymptotic theory, including root-n normality, for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effect (ATE). In order to show the practical relevance of the proposed framework, we conduct a sensitivity analysis via extensive Monte Carlo simulation exercises. The results suggest that the estimates of the parameters, especially the ATE, are sensitive to parametric specification, while semiparametric estimation exhibits robustness to underlying data generating processes. We then provide an empirical illustration where we estimate the effect of health insurance on doctor visits. In this paper, we also show that the absence of excluded instruments may result in identification failure, in contrast to what some practitioners believe.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen, X (2007) Large sample sieve estimation of semi-nonparametric models | 1.000 | 14 | 4 | 100% |
| 2 | Joe, H (1997) Multivariate Models and Multivariate Dependence Concepts | 0.737 | 3 | 2 | 100% |
| 3 | Marra, G. and R. Radice (2011) Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity | 0.737 | 3 | 2 | 100% |
| 4 | Rhine, S. L., W. H. Greene, and M. Toussaint-Comeau (2006) The importance of check-cashing businesses to the unbanked: Racial/ethnic differences | 0.737 | 3 | 2 | 100% |
| 5 | Shaikh, A. M. and E. J. Vytlacil (2011) Partial identification in triangular systems of equations with binary dependent variables | 0.737 | 3 | 2 | 100% |
| 6 | Han, S. and E. Vytlacil (2017) Identification in a generalization of bivariate probit models with dummy endogenous regressors self | 0.644 | 2 | 2 | 100% |
| 7 | Bierens, H. J (2014) Consistency and asymptotic normality of sieve ml estimators under low-level conditions | 0.644 | 2 | 2 | 100% |
| 8 | Freyberger, J. and M. Masten (2015) Compactness of infinite dimensional parameter spaces | 0.644 | 2 | 2 | 100% |
| 9 | Lorentz, G (1966) Approximation of functions | 0.644 | 2 | 2 | 100% |
| 10 | Mourifié, I. and R. Méango (2014) A note on the identification in two equations probit model with dummy endogenous regressor | 0.644 | 2 | 2 | 100% |
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