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Author: Koop, Gary
Resulting in 4 citations.
1. Koop, Gary
Tobias, Justin L.
Learning about Heterogeneity in Returns to Schooling
Journal of Applied Econometrics 19,7 (November-December 2004): 827-849.
Also: http://www3.interscience.wiley.com/cgi-bin/fulltext/107636928/HTMLSTART
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Bayesian; Educational Returns; Heterogeneity

Permission to reprint the abstract has not been received from the publisher.

Using data from the National Longitudinal Survey of Youth (NLSY) we introduce and estimate various Bayesian hierarchical models that investigate the nature of unobserved heterogeneity in returns to schooling. We consider a variety of possible forms for the heterogeneity, some motivated by previous theoretical and empirical work and some new ones, and let the data decide among the competing specifications. Empirical results indicate that heterogeneity is present in returns to education. Furthermore, we find strong evidence that the heterogeneity follows a continuous rather than a discrete distribution, and that bivariate normality provides a very reasonable description of individual-level heterogeneity in intercepts and returns to schooling. Copyright (C) 2004 John Wiley Sons, Ltd.
Bibliography Citation
Koop, Gary and Justin L. Tobias. "Learning about Heterogeneity in Returns to Schooling." Journal of Applied Econometrics 19,7 (November-December 2004): 827-849.
2. Koop, Gary
Tobias, Justin L.
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]
Bibliography Citation
Koop, Gary and Justin L. Tobias. "Semiparametric Bayesian Inference in Smooth Coefficient Models." Journal of Econometrics 134,1 (September 2006): 283-315.
3. Koop, Gary
Tobias, Justin L.
Semiparametric Bayesian Inference in Smooth Coefficient Models
Working Paper No. 04/18, Department of Economics, University of Leicester, October 2003
Cohort(s): NLSY79
Publisher: Department of Economics, University of Leicester
Keyword(s): Bayesian; Cognitive Ability; Educational Returns; Endogeneity; 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 both cross sectional and longitudinal data smooth coefficient models (with and without endogeneity problems). 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 - estimation, testing and smoothing parameter selection can be carried out analytically in the cross-sectional smooth coefficient model, and estimation in the hierarchical models only involves simulation from standard distributions.

We apply our methods by fitting several hierarchical models using data from the National Longitudinal Survey of Youth (NLSY). We explore the relationship between ability and log wages and flexibly model how returns to schooling vary with measured cognitive ability. In a generalization of this model, we also permit endogeneity of schooling and describe simulation-based methods for inference in the presence of the endogeneity problem. We find returns to schooling are approximately constant throughout the ability support and that simpler (and often used) parametric specifications provide an adequate description of these relationships.

Bibliography Citation
Koop, Gary and Justin L. Tobias. "Semiparametric Bayesian Inference in Smooth Coefficient Models." Working Paper No. 04/18, Department of Economics, University of Leicester, October 2003.
4. Koop, Gary
Tobias, Justin L.
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.

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.