Robust Inference in Sample Selection Models

Estimation of Sample Selection Models with Spatial Dependence

Monte Carlo evidence on the choice between sample selection and two-part models

Problem statement: It is well known that, the standard approach to estimating a sample selection models shows an inconsistent estimation results if the distributional assumption are incorrect. Approach: An important progress in the last decade to develop an alternative to overcome the deficiency is through the used of semi-parametric method. However, the usage of semi-parametric approach still does not cover the deficiency of the model. Results: We introduced a fuzzy membership function for solving uncertainty data of a sample selection model and employed method for sample selection models, that is, the two-step estimators to estimate a model of the so-called the self-selection decision. Fuzzy Parametric of Sample Selection Model (FPSSM) is builds as a hybrid to the conventional parametric sample selection model. Conclusion/Recommendations: The result showed that as a whole, the FPSSM give a better estimate and consistent when compared to the Parametric of Sample Selection Model (PSSM). This application demonstrate that the proposed fuzzy modeling approach was quite reasonable and provides an important and significant finding compared with conventional method especially in terms of estimation and consistency.

Semiparametric and nonparametric estimation of sample selection models under symmetry

Heckman’s (Ann Econ Soc Meas 4(5), 475–492, 1976; Econometrica 47, 153–161, 1979) sample selection model has been employed in three decades of applications of linear regression studies. This paper builds on this framework to obtain a sample selection correction for the stochastic frontier model. We first show a surprisingly simple way to estimate the familiar normal-half normal stochastic frontier model using maximum simulated likelihood. We then extend the technique to a stochastic frontier model with sample selection. In an application that seems superficially obvious, the method is used to revisit the World Health Organization data (WHO in The World Health Report, WHO, Geneva 2000; Tandon et al. in Measuring the overall health system performance for 191 countries, World Health Organization, 2000) where the sample partitioning is based on OECD membership. The original study pooled all 191 countries. The OECD members appear to be discretely different from the rest of the sample. We examine the difference in a sample selection framework.

This paper introduces the researches and applications based on genetic algorithm in the sample selection of papers. We followed the steps of the work and separate the model of four sub models: the pre-process model, sample selection model, property model and reefed model. We consider form all aspect which may affects the decision of sample selection and solve them. Form the instance, the genetic algorithm we discussed can improve the reliability and efficiency of the national first degree architecture test on some degree.

Modeling households' behavior with the data from a contingentvaluation (CV) survey is often complicated by samplenon-response, which can cause non-response bias and sampleselection bias, leading to inconsistent parameter estimates and adistorted mean willingness-to-pay estimate. This paper reportsthe results of empirical tests for both biases using householdsurvey data in which the double-bounded dichotomous choice CVquestion involved the benefit of a tap water quality improvementpolicy in Korea. No non-response bias, but sample selection bias,is detected in the sample. To correct for sample selection bias,a sample selection model is employed. The authors also discusshow failure to correct for bias may distort aggregate benefitestimates.

Public perception of new energy vehicles: Evidence from willingness to pay for new energy bus fares in China

Summary Sample selection models are important for correcting the effects of non-random sampling. This paper is about semiparametric estimation using a series approximation to the correction term. Regression spline and power series approximations are considered. Asymptotic normality and consistency of an asymptotic variance estimator are shown.

This article introduces semiparametric methods for the estimation of simultaneous-equation microeconometric models with index restrictions. The methods are motivated by a semiparametric minimum-distance procedure, which unifies the estimation of both regression-type and linear or nonlinear simultaneous-equation models without emphasis on the construction of instrumental variables. Single-equation and systematic estimation methods and optimal weighting procedures are considered. The estimators are √n-consistent and asymptotically normal. For the estimation of nonparametric regression and some sample selection models where the variances of disturbances are functions of the same indices, the optimal weighted estimator attains Chamberlain's efficient bound for models with conditional moment restrictions. The weighted estimator is shown to be optimal within a class of semiparametric instrumental variables estimators. JEL classification numbers: C14, C24, C34.

Bounding quantiles in sample selection models

We develop a fully parametric estimation procedure for unbalanced panel data models with unobserved effects that allow for a common binary endogenous variable in both the selection equation and the outcome equation. We test the finite sample properties of the estimator using Monte Carlo simulations and find that our estimator performs better in the presence of high endogeneity and high sample selection compared to the estimators that ignore either or both of these issues. In addition, our estimator is also robust to distributional misspecification. We apply our econometric methodology to estimate the effect of fertility decisions on wages for white women using the National Longitudinal Survey of Youth 1979 (NLSY79) data from 1982 to 2006.

Heckman’s sample selection model was applied to data from the Malaysian Household Expenditure Survey 2004/2005 to examine the factors influencing the likelihood of purchasing and the amount spent on alcohol in Malaysia. Results of the marginal effects suggest that while socio-demographic factors are important determinants of household purchase decisions and expenditure levels on alcohol in Malaysia, the effects vary across ethnic groups. Specifically, although education had a significant but modest impact in reducing the probability of alcohol purchases and expenditure levels among ethnic Chinese households, this effect was not evident among the ethnic Indians and other races. While increasing household size lowered the likelihood of purchasing alcohol and its expenditure levels for all ethnic groups, the reinforcing effects of both income and gender were relevant only for ethnic Chinese and Indian households. Last, urban Indian households were less likely to purchase alcohol and spend less compared to rural Indian households.

Vocational education and training (VET) is critical in developing skilled manpower resources in a country. However, in India, where various administrative and institutional factors are key in the determination of employment and wages, people from all social groups may not benefit equally, from VET. This study analyses how the impact of VET on employment and wages varies across social groups in the Indian manufacturing sector. The main data source for this study is the Employment and Unemployment Survey in India (10th Schedule) of the 68th National Sample Survey quinquennial round (2011–2012). To tackle the problem of bias in sample selection, this study uses Heckman’s Sample Selection Model (1979) with the two-steps estimation technique (Heckit). It reveals that VET significantly enhances participation from all social groups in the manufacturing sector and aggregates wages, but is ineffective in certain manufacturing industries. In certain cases, VET variously impacts wages across workers from different castes and ethnicities.

The problem of specification errors in sample selection models has received considerable attention both theoretically and empirically. Originally proposed in Heckman (1979) within a fully parametric context, Ahn and Powell (1993) have proposed a semiparametric alternative that is claimed to be asymptotically robust against several misspecification errors. However, very few is known about the finite sample behavior of these estimators. In this paper we investigate theoretically and by simulations both bias and finite sample distribution of these estimators when ignoring heteroskedasticity in the sample selection mechanism.