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Title: Semiparametric Bayesian Inference in Smooth Coefficient Models
Resulting in 1 citation.
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Koop, Gary Tobias, Justin L. |
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Semiparametric Bayesian Inference in Smooth Coefficient Models Staff General Research Papers 12202, Department of Economics, Iowa State University, July 2004. Also: http://www.econ.iastate.edu/faculty/tobias/documents/smoothrev2.pdf Cohort(s): NLSY79 Publisher: Department of Economics, Iowa State University Keyword(s): Bayesian; Cognitive Ability; Educational Returns; Labor Supply; Modeling; Schooling; Variables, Independent - Covariate Permission to reprint the abstract has not been received from the publisher. We describe procedures for Bayesian estimation and testing in cross sectional, panel data and nonlinear smooth coefficient models. The smooth coefficient model is a generalization of the partially linear or additive model wherein coefficients on linear explanatory variables are treated as unknown functions of an observable covariate. In the approach we describe, points on the regression lines are regarded as unknown parameters and priors are placed on differences between adjacent points to introduce the potential for smoothing the curves. The algorithms we describe are quite simple to implement - for example, estimation, testing and smoothing parameter selection can be carried out analytically in the cross-sectional smooth coefficient model. We apply our methods using data from the National Longitudinal Survey of Youth (NLSY). Using the NLSY data we first explore the relationship between ability and log wages and flexibly model how returns to schooling vary with measured cognitive ability. We also examine model of female labor supply and use this example to illustrate how the described techniques can been applied in nonlinear settings. |
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Bibliography Citation
Koop, Gary and Justin L. Tobias. "Semiparametric Bayesian Inference in Smooth Coefficient Models." Staff General Research Papers 12202, Department of Economics, Iowa State University, July 2004. |