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Title: Semiparametric Bayesian Inference in Smooth Coefficient Models
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Koop, Gary Tobias, Justin L. |
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Semiparametric Bayesian Inference in Smooth Coefficient Models Journal of Econometrics 134,1 (September 2006): 283-315. Also: http://www.sciencedirect.com/science/article/pii/S0304407605001491 Cohort(s): NLSY79 Publisher: Elsevier Keyword(s): Bayesian; Cognitive Ability; Education; Labor Supply; Modeling; Variables, Independent - Covariate 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 a model of female labor supply and use this example to illustrate how the described techniques can been applied in nonlinear settings. [ABSTRACT FROM AUTHOR; Copyright 2006 Elsevier] |
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Bibliography Citation
Koop, Gary and Justin L. Tobias. "Semiparametric Bayesian Inference in Smooth Coefficient Models." Journal of Econometrics 134,1 (September 2006): 283-315.
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