Abstract
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.
Highlights
Sample selection is an econometric model that has been found interesting application in empirical studies
Applying the Fuzzy Parametric of Sample Selection Model (FPSSM) gave the most significant result when compared to the Parametric of Sample Selection Model (PSSM), the coefficient estimated for variables EDU, PEXP, PEXP2 and PEXPCHD gave a small standard error of the coefficient estimate
The FPSSM give a better estimate when compared to the PSSM
Summary
Sample selection is an econometric model that has been found interesting application in empirical studies. Known as ‘self-selection’ or ‘selectivity’ gives a good prior knowledge about relationships and provides an ideal way to incorporate expert judgment and quantitative information. Selection can occur in a linear regression model when data on the dependent variable are missing non-randomly conditional on the independent variables. When observations are selected which are not independent of the outcome variables of the study, this sample selection leads to biased inferences. Problems arise when the researcher fails to observe a random sample of a population of interest. Model with parametric distributions is subject to distributional misspecifications and tends to result in inconsistent estimates
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