ESTIMASI PARAMETER PADA MODEL SELEKSI SAMPEL HECKMAN DENGAN KOVARIAT ENDOGEN MENGGUNAKAN PENDEKATAN KEMUNGKINAN MAKSIMUM INFORMASI PENUH

Estimation of Sample Selection Models with Spatial Dependence

A semiparametric likelihood method is proposed for the estimation of sample selection models. The method is a two-step semiparametric scoring estimation procedure based on an index restriction and kernel estimation. Under some regularity conditions, the estimator is square-root n-consistent and asymptotically normal. The estimator is also asymptotically efficient in the sense that its asymptotic covariance matrix attains the semiparametric efficiency bound under the index restriction. For the binary choice sample selection model, it also attains the efficiency bound under the independence assumption. This method can be applied to the estimation of general sample selection models.

Marginal Effects and Significance Testing with Heckman's Sample Selection Model: A Methodological Note

This article illustrates two techniques for calculating the statistical significance of the marginal effects derived from Heckman's sample selection model, an increasingly common econometric specification in economics and political science. The discussion draws on an analysis by Sweeney (2003) of the incidence and intensity of interstate disputes. After replicating his results, the article presents the delta method and the nonparametric bootstrap as alternative techniques for obtaining SEs of the marginal effects, which themselves are calculated from a transformation of the model parameters. The analysis reveals two variables for which misleading inferences are drawn with respect to the precision of the estimated coefficients in the original study, suggesting that significance testing of the derived marginal effects is warranted.

The two-part model and Heckman's sample selection model are often used in economic studies which involve analyzing the demand for limited variables. This study proposed a simultaneous equation model (SEM) and used the expectation-maximization algorithm to obtain the maximum likelihood estimate. We then constructed a simulation to compare the performance of estimates of price elasticity using SEM with those estimates from the two-part model and the sample selection model. The simulation shows that the estimates of price elasticity by SEM are more precise than those by the sample selection model and the two-part model when the model includes limited independent variables. Finally, we analyzed a real example of cigarette consumption as an application. We found an increase in cigarette price associated with a decrease in both the propensity to consume cigarettes and the amount actually consumed.

Two-step estimation of heteroskedastic sample selection models

Semiparametric and nonparametric estimation of sample selection models under symmetry

Estimation of spatial sample selection models: A partial maximum likelihood approach

This paper presents a mathematical model used to determine jointly a worker's decision to participate in a nonhome and nonwork activity and the decision on how long to participate. With the household interview survey data from the New York City area, the workers’ participation and duration decisions were estimated for each of five periods in a worker's day: before morning commute, morning commute, midday, evening commute, and after evening commute. To account for the censored nature of the duration data (i.e., a large number of observations clustered at zero), Heckman's sample selection model was used together with the full information maximum likelihood estimation method. To enhance the behavioral basis for the models, extensive statistical tests were given to the model specifications and the assumptions underlying the model structure. The empirical results provide useful insights into the effects of socio-demographics, land use–transportation measures, and activity duration characteristics on workers’ daily scheduling of nonwork activities and travel in a highly urbanized environment. This study also provides exploratory methodologic evidence that could lead to an approach for predicting the change in a worker's nonwork activity patterns (participation and duration) as a result of changes in future demographic conditions and land use–transportation scenarios.

This paper presents a mathematical model used to determine jointly a worker's decision to participate in a nonhome and nonwork activity and the decision on how long to participate. With the household interview survey data from the New York City area, the workers' participation and duration decisions were estimated for each of five periods in a worker's day: before morning commute, morning commute, midday, evening commute, and after evening commute. To account for the censored nature of the duration data (i.e., a large number of observations clustered at zero), Heckman's sample selection model was used together with the full information maximum likelihood estimation method. To enhance the behavioral basis for the models, extensive statistical tests were given to the model specifications and the assumptions underlying the model structure. The empirical results provide useful insights into the effects of socio-demographics, land use-transportation measures, and activity duration characteristics on workers' d...

Bivariate symmetric Heckman models and their characterization

Using data from a survey sample of people 65 years of age and older living in Seoul and Chuncheon, Korea, this paper assesses whether the level of social capital affects elderly individuals' use of medical care. As an econometric model, Heckman's Sample Selection model and the 2SLS method were used to control the endogeneity problem of patient's trust on doctors. The results of our estimations indicate that the level of social capital exerts a positive effect on elderly individuals' use of medical care indirectly, via its positive effect on the level of trust in doctors.

Market Discipline and the Russian Interbank Market

With a focus on individual motorists in car-owning households in Germany, this analysis econometrically investigates the determinants of automobile travel for nonwork service activities against the backdrop of two questions: (a) Does gender play a role in determining the probability of car use and the distance driven? and (b) If so, how is this role mitigated or exacerbated by other socioeconomic attributes of the individual and the household in which he or she resides? Drawing on a panel of data collected between 1996 and 2003, Heckman's sample selection model is specified to control for biases that otherwise could arise from the existence of unobservable variables that determine both the discrete and the continuous choices pertaining to car use. The results indicate that although women, on average, undertake more nonwork travel than men, they undertake less such travel by car, implying a greater reliance on other modes. Moreover, employment status, age, the number of children, automobile availability, and the proximity to public transit are all found to have significantly different effects on the probability of nonwork car travel between men and women but—with the exception of automobile availability—not on the distance driven. Taken together, these results suggest that policies targeted at reducing automobile dependency and associated negative externalities, such as congestion, are unlikely to have uniform effects across the sexes. These findings have implications for both policy evaluation and travel demand forecasting.

Market Discipline and the Russian Interbank Market