An empirical investigation of parametric and semiparametric estimation methods in sample selection models

In this paper we analyze empirically different specifications of a sample selection model. We are interested in how the estimates vary across alternative assumptions concerning the joint conditional distribution of the sample selection equation errors, such us the specification of error distribution, the functional relationship of the index function and heteroskedasticity. To do this, we estimate a wage equation for the Spanish labor market using two different approaches: Maximum Likelihood and Two-Step Methods. For the latter, three alternative semiparametric procedures are used to compute the sample selection mechanism, and thus three alternative two-step estimators of the parameters of the wage equation are obtained. We compare theses estimates with Heckman's approach

Nonparametric and Semiparametric Estimation of Additive Models with both Discrete and Continuous Variables under Dependence

This paper is concerned with the estimation and inference of nonparametric and semiparametric additive models in the presence of discrete variables and dependent observations. Among the different estimation procedures, the method introduced by Linton and Nielsen, based in marginal integration, has became quite popular because both its computational simplicity and the fact that it allows an asymptotic distribution theory. Here, an asymptotic treatment of the marginal integration estimator under different mixtures of continuous-discrete variables is offered, and furthermore, in the semiparametric partially additive setting, an estimator for the parametric part that is consistent and asymptotically efficient is proposed. The estimator is based in minimizing the L2 distance between the additive nonparametric component and its correspondent linear direction. Finally, we present an application to show the feasibility of all methods introduced in the paper

Semiparametric three step estimation methods in labor supply models

The aim of this paper is to provide an alternative way of specification and estimation of a labor supply model. The proposed estimation procedure can be included in the so called predicted wage methods and its main interest is twofold .. First, under standard assumptions in studies of labor supply, the estimator based on predicted wages is shown to be consistent and asymptotically normal. Moreover, we propose also a consistent estimator of the asymptotic covariance matrix. In the main part of the paper we introduce a semiparametric estimator based on marginal integration techniques that allows for nonlinear relationships between the labor supply variable and its covariates. We show that also the wage equation could be modeled nonparametrically. The asymptotic properties of the estimators are given. Finally, in a detailed application we compare the results empirically against those obtained in standard three step estimators based on predicted wages

Specification testing when the null is nonparametric or semiparametric.

This paper discusses the problem of testing misspecifications in semiparametric regression models for a large family of econometric models under rather general conditions. We focus on two main issues that typically arise in econometrics. First, many econometric models are estimated through maximum likelihood or pseudoML methods like, for example, limited dependent variable or gravity models. Second, often one might not want to fully specify the null hypothesis. Instead, one would rather impose some structure like separability or monotonicity. In order to address these points we introduce an adaptive omnibus test. Special emphasis is given to practical issues like adaptive bandwidth choice, general but simple requirements on the estimates, and finite sample performance, including the resampling approximations.We acknowledge nancial support from FUNCAS, the Spanish Projects MTM2008-03010 and ECO2010-15455, and the DAAD Project 50119348

‘De lezer moet het zelf maar bepalen.’ Onderzoek naar het werk en de zelfrepresentatie van A.H.J. Dautzenberg

Schrijver A.H.J. Dautzenberg staat erom bekend dat hij zowel in zijn boeken als daarbuiten een spel speelt met fictie en werkelijkheid. In dit onderzoek wordt gekeken op welke wijze de romans van schrijver A.H.J. Dautzenberg een idee van authenticiteit produceren en in welke mate de zelfrepresentatie van de schrijver en de receptie van zijn werk daaraan bijdragen. In deze masterscriptie worden drie boeken van Dautzenberg geanalyseerd: de autobiografische romans Samaritaan (2011) en Extra tijd (2012) en de briefroman De Fictiefabriek (2014), een boek dat Dautzenberg met ex-wetenschapper Diederik Stapel schreef. Omdat Dautzenberg zijn spel met fictie en werkelijkheid ook buiten zijn literaire werk voortzet, worden ook de (deels) verzonnen interviews belicht die de schrijver voor de VPRO Gids schreef. In dit onderzoek worden, zoals Edwin Praat doet in zijn werk Verrek, het is geen kunstenaar, de werelden van binnen en buiten het boek op elkaar betrokken. De romans van Dautzenberg worden geanalyseerd in samenhang met de zelfrepresentatie van de schrijver en de receptie van zijn werk. Als basis voor de analyse is het boek Oprecht gelogen (2013) van Lut Missinne gebruikt. Aan de hand hiervan is geanalyseerd welke tekstuele kenmerken in het werk van A.H.J. Dautzenberg ervoor kunnen zorgen dat de lezer het verhaal van de schrijver als ‘authentiek’ en ‘oprecht’ beschouwt. Volgens Missinne kan de retorische overtuigingskracht van de schrijver een authenticiteitseffect teweegbrengen. Zij stelt dat de schrijver door het inzetten van ‘echtheidssignalen’ en ‘fictionaliteitssignalen’ de lectuur van de lezer kan sturen. De lectuur van de lezer staat in dit onderzoek centraal. Aan de hand van het werk van Missinne en het boek Ethos and Narrative Interpretation van Liesbeth Korthals Altes, wordt verantwoord dat de lezer uiteindelijk bepaalt of hij of zij het verhaal als authentiek en oprecht beschouwt. Uit de analyse naar de tekstuele elementen in de boeken van Dautzenberg blijkt dat de schrijver door het inzetten van echtheidssignalen een idee van authenticiteit produceert, maar dat dit idee van authenticiteit vervolgens weer wordt geproblematiseerd door de vele fictionaliteitssignalen. Door ook te kijken naar de schrijver buiten het boek, is geconcludeerd dat het mediaoptreden en de zelfrepresentatie van Dautzenberg van grote invloed zijn op de mate waarin zijn werk als ‘authentiek’ bestempeld wordt. Dautzenberg kan een idee van authenticiteit produceren wanneer hij zowel in zijn boeken als in zijn zelfrepresentatie retorische overtuigingsmiddelen inzet

Nonparametric and Semiparametric Estimation of Additive Models with both Discrete and Continuous Variables under Dependence

This paper is concerned with the estimation and inference of nonparametric and semiparametric additive models in the presence of discrete variables and dependent observations. Among the different estimation procedures, the method introduced by Linton and Nielsen, based in marginal integration, has became quite popular because both its computational simplicity and the fact that it allows an asymptotic distribution theory. Here, an asymptotic treatment of the marginal integration estimator under different mixtures of continuous-discrete variables is offered, and furthermore, in the semiparametric partially additive setting, an estimator for the parametric part that is consistent and asymptotically efficient is proposed. The estimator is based in minimizing the L2 distance between the additive nonparametric component and its correspondent linear direction. Finally, we present an application to show the feasibility of all methods introduced in the paper

An Empirical Investigation of Parametric and Semiparametric Estimation Methods in Sample Selection Models // Investigación empírica de métodos de estimación paramétricos y semiparamétricos de modelos de selección muestral

In this paper we analyze empirically different specifications of a sample selection model. We are interested in how the estimates vary across alternative assumptions concerning the joint conditional distribution of the sample selection equation errors, such us the specification of error distribution, the functional relationship of the index function and heteroskedasticity. To do this, we estimate a wage equation for the Spanish labor market using two different approaches: Maximum Likelihood and Two-Step Methods. For the latter, three alternative semiparametric procedures are used to compute the sample selection mechanism, and thus three alternative two-step estimators of the parameters of the wage equation are obtained. We compare theses estimates with Heckman's approach.------------------------------------En este trabajo se analizan empíricamente distintas especificaciones de un modelo de selección muestral. Estamos interesados en conocer cómo las estimaciones de los parámetros varían en función de supuestos alternativos sobre la distribución condicional conjunta de los errores de la ecuación de selección, de la forma funcional de la función índice y la heteroscedasticidad. Para el análisis, estimamos una ecuación de salarios para el mercado de trabajo español usado dos enfoques distintos: máxima-verosimilitud y métodos en dos etapas. Para el caso de la estimación en etapas, consideramos tres procedimientos semiparamétricos alternativos para el cómputo del mecanismo de selección. Así, se obtienen tres estimadores en dos etapas de los parámetros de la ecuación de salarios. Comparamos las estimaciones con la obtenidas siguiendo el método de Heckman

An Empirical Investigation of Parametric and Semiparametric Estimation Methods in Sample Selection Models = Investigación empírica de métodos de estimación paramétricos y semiparamétricos de modelos de selección muestral

In this paper we analyze empirically different specifications of a sample selection model. We are interested in how the estimates vary across alternative assumptions concerning the joint conditional distribution of the sample selection equation errors, such us the specification of error distribution, the functional relationship of the index function and heteroskedasticity. To do this, we estimate a wage equation for the Spanish labour market using two different approaches: Maximum Likelihood and Two-Step Methods. For the latter, three alternative semiparametric procedures are used to compute the sample selection mechanism, and thus three alternative two-step estimators of the parameters of the wage equation are obtained. We compare theses estimates with Heckman's approach. En este trabajo se analizan empíricamente distintas especificaciones de un modelo de selección muestral. Estamos interesados en conocer cómo las estimaciones de los parámetros varían en función de supuestos alternativos sobre la distribución condicional conjunta de los errores de la ecuación de selección, de la forma funcional de la función índice y la heteroscedasticidad. Para el análisis, estimamos una ecuación de salarios para el mercado de trabajo español usado dos enfoques distintos: máxima-verosimilitud y métodos en dos etapas. Para el caso de la estimación en etapas, consideramos tres procedimientos semiparamétricos alternativos para el cómputo del mecanismo de selección. Así, se obtienen tres estimadores en dos etapas de los parámetros de la ecuación de salarios. Comparamos las estimaciones con la obtenidas siguiendo el método de Heckman.sample selection models; distributional assumptions; semiparametric two-step estimation methods; modelos de selección muestral; hipótesis distribucionales; métodos de estimación en dos etapas semiparamétricos

Low dimensional semiparametric estimation in a censored regression model

A new estimation procedure for a partial linear additive model with censored responses is proposed. To this aim, ideas of Lewbel and Linton [A. Lewbel, O. Linton, Nonparametric censored and truncated regression, Econometrica 70 (2002) 765-779] on censored model regression are combined with those of Kim et al. [W. Kim, O. Linton, N.W. Hengartner, A computationally efficient estimator for additive nonparametric regression with bootstrap confidence intervals, Journal of Computational and Graphical Statistics, 8 (1999) 278-297] on marginal integration and those on average derivatives. This allows for dimension reduction, interpretability and -- depending on the context -- for weights yielding computationally attractive estimates. Asymptotic behavior is provided for all proposed estimators.Semiparametric censored regression Partial linear additive models Marginal integration