The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification
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Publication:1740276
DOI10.1016/j.jeconom.2018.07.009zbMath1452.62942OpenAlexW2555606026WikidataQ128742282 ScholiaQ128742282MaRDI QIDQ1740276
D. S. Poskitt, Chuhui Li, Xue-yan Zhao
Publication date: 30 April 2019
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp16-16.pdf
copulainstrumental variablesmisspecificationaverage treatment effectidentified setbinary outcome models
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- Quasi-likelihood and its application. A general approach to optimal parameter estimation
- Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables
- Identification of multiple equation probit models with endogenous dummy regressors
- A note on the identification in two equations probit model with dummy endogenous regressor
- Identification in a generalization of bivariate probit models with dummy endogenous regressors
- Partial Identification in Triangular Systems of Equations With Binary Dependent Variables
- Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity
- Instrumental Variable Models for Discrete Outcomes
- Dummy Endogenous Variables in a Simultaneous Equation System
- Statistical Applications of the Multivariate Skew Normal Distribution
- The multivariate skew-normal distribution
- Monotone Instrumental Variables: With an Application to the Returns to Schooling
- Generalized Instrumental Variable Models
- A Simple Estimator for Binary Choice Models with Endogenous Regressors
- Nonparametric Identification under Discrete Variation
- Maximum Likelihood Estimation of Misspecified Models
