Estimation of limited dependent variable models by ordinary least squares and the method of moments

DOI10.1016/0304-4076(83)90013-1zbMath0509.62104OpenAlexW2031803719MaRDI QIDQ1838267

William H. Greene

Publication date: 1983

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4076(83)90013-1

zbMATH Keywords

maximum likelihood estimatormethod of momentsordinary least squaresprobittobittruncated regressionasymptotic covariance matricesestimation of limited dependent variable modelstwo-limit probit

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05)

Related Items (8)

Estimation and inference with censored and ordered multinomial response data ⋮ The Logit Model in Economics ⋮ Estimation of the correlation coefficient in a bivariate probit model using the method of moments ⋮ Bayesian Tobit quantile regression with single-index models ⋮ RECENTERED AND RESCALED INSTRUMENTAL VARIABLE ESTIMATION OF TOBIT AND PROBIT MODELS WITH ERRORS IN VARIABLES ⋮ Estimation of censored linear errors-in-variables models ⋮ Tobit models: A survey ⋮ Small sample performance of large sample tests for tobit versus^p-tobit

Cites Work

• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
• Using Least Squares to Approximate Unknown Regression Functions
• On the Asymptotic Bias of the Ordinary Least Squares Estimator of the Tobit Model
• Approximating a Truncated Normal Regression with the Method of Moments
• Estimation of the Two-Limit Probit Regression Model
• Sample Selection Bias as a Specification Error
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