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.
| Original language | English |
|---|---|
| Pages (from-to) | 91-113 |
| Number of pages | 20 |
| Journal | Journal of Econometrics |
| Volume | 209 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Mar 2019 |
Keywords
- Average treatment effect
- Binary outcome models
- Copula
- Identified set
- Instrumental variables
- Misspecification
Cite this
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The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification. / Li, Chuhui; Poskitt, D.S.; Zhao, Xueyan.
In: Journal of Econometrics, Vol. 209, No. 1, 03.2019, p. 91-113.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification
AU - Li, Chuhui
AU - Poskitt, D.S.
AU - Zhao, Xueyan
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Average treatment effect
KW - Binary outcome models
KW - Copula
KW - Identified set
KW - Instrumental variables
KW - Misspecification
UR - http://www.scopus.com/inward/record.url?scp=85058795974&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2018.07.009
DO - 10.1016/j.jeconom.2018.07.009
M3 - Article
AN - SCOPUS:85058795974
VL - 209
SP - 91
EP - 113
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 1
ER -