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Estimation of ordered response models with sample selection
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DOI：10.1177/1536867X1101100204.png)
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摘要
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We introduce two new Stata commands for the estimation of an ordered response model with sample selection. The opsel command uses a standard maximum-likelihood approach to lit a parametric specification of the model where errors are assumed to follow a bivariate Gaussian distribution. The snpopsel command uses the semi-nonparametric approach of Gallant and Nychka. (1987,Econornetrica. 55: 363-390) to fit a semiparametric specification of the model where the bivariate density function of the errors is approximated by a Hermite polynomial expansion. The snpopsel command extends the set of Stata routines for semi-nonparametric estimation of discrete response models. Compared to the other semi-nonparametric estimators, our routine is relatively faster because it is programmed in Mata. In addition, we provide new postestimation routines to compute linear predictions, predicted probabilities, and marginal effects. These improvements are also extended to the set of semi-nonparametric Stata commands originally written by Stewart (2004, Stata journal 4: 27-39) and De Luca (2008, Stata journal 8: 190-220). An illustration of the new opsel and snpopsel commands is provided through an empirical application on self-reported health with selectivity due to sample attrition.
Keyword:
st0226
opsel
opsel postestimation
snoop
sneop postestimation
snp2
snp2 postestimation
snp2s
snp2s postestimation
snpopsel
snpopsel postestimation
snp
snp postestimation
ordered response models
sample selection
parametric maximum-likelihood estimation
semi-nonparametric estimation
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2.4
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1.2K
被引数:
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Maximum likelihood estimation of endogenous switching and sample selection models for binary, ordinal, and count variables.png)
STATA JOURNALOAAI
STATA JOURNALIF2.4
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ECONOMETRICAIF7.1
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