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Semi-parametric estimation of the sample selection model
TLDR
In this article, the authors compare three types of nonparametric Maxfmum Zikelíhood estimators: Semt-nonparametrc MaxfmUM Zikellíhood, Semtnonparameter Zikeller estimators, and SingZe-equatton estimators.Abstract:
Esttmatfng a uxtge equatfon, account must be taken of the jact that ruages oj non-i,iorkers are not observed. For thts purpose, Heckman (1979) fntroduced the sample seLectfon modeL, consiating of troo equatíons: A(Línear) arage equatfon, explafning the potenttal Zog t,iage rate of every índívídual, incZudfng non-morkers, and a bfnary choice equatton, indícatíng ~uhether or not someone fs employed and the axige fa observed. TradíttonaL ML-eatfmatfon requfres a parametric specfftcatfon of the dtstributfon of the error terms, such as bivarfate normaZíty. Recently, a number of aemt-parametric estfmators have been developed mhích onZy requfre fndependence of the errors from the regressora ín both equations. We conatder three types o1 them: Semt-nonparametrtc Maxfmum Zikelíhood, ín mhich the parametera of interest and the densíty oJ the errors are eatimated afmuLtaneouaZy; 1}uo atage estfmators, generalizing the traditfonal Heckman ttoo step eatimator, míth semf-parametric estfmates o~ the bfnary chotce equatfon and a noriparametrfc correctton term added to the axige equatfon; and singZe-equatton estimators, neglecting irlJormation on non-raorkers. The estimators conaídered are asymptotícaZly normal, and aZZom Jor fnference. We preaent reaulta ,~or a sample of Dutch jemales and compare ~ith parametric !(L-estimatea. " We are grateful to the Netherlends Central Bureau of Statiatics (CBS) for providing the data. The views expressed i n this paper do not necessarily reflect the policies of the CBS. Financial support by the Netherlands Organisation of Scientific Research (NWO) and the Royal Netherlands Academy of Arts and Sciences (KNAW) is gratefully acknowledged by the first and second author, respectively. We are grateful for valuable commenta to Arie Kapteyn and seminar participants at CORE, (ironingen University, Tilburg University, and the Free University of Amsterdam.read more
Citations
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Journal ArticleDOI
Estimating Models with Sample Selection Bias: A Survey
TL;DR: In this paper, a survey of the available methods for estimating models with sample selection bias is presented, including semi-parametric and fully parameterized models, and the ability to tackle different selection rules generating the selection bias.
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Endogeneity in nonparametric and semiparametric regression models
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TL;DR: In this article, the authors consider the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors and identify the "average structural function" as a parameter of central interest.
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Chapter 41 Estimation of semiparametric models
TL;DR: Semi-parametric models as mentioned in this paper combine a parametric form for some component of the data generating process (usually the behavioral relation between the dependent and explanatory variables) with weak nonparametric restrictions on the remainder of the model, usually the distribution of the unobservable errors.
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The public and private sector pay gap in Pakistan: a quantile regression analysis
Asma Hyder,Barry Reilly +1 more
TL;DR: In this article, the authors examined the magnitude of public/private wage differentials in Pakistan using data drawn from the 2001-02 Pakistan National Labour Force Survey and found that about two-fifths of the raw wage differential in average hourly wages between the two sectors is accounted for by differentially in average characteristics.
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Testing the normality assumption in the sample selection model with an application to travel demand
B. van der Klaauw,Ruud H. Koning +1 more
TL;DR: In this article, a test for the normality assumption in the sample selection model is introduced, based on a flexible parametric specification of the density function of the error terms in the model.
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