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Semiparametric Bayesian Inference in Smooth Coefficient Models

  • Koop, Gary M
  • Tobias, Justin

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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Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 12202.

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Date of creation: 01 Jan 2006
Date of revision:
Publication status: Published in Journal of Econometrics 2006,
Handle: RePEc:isu:genres:12202
Contact details of provider: Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
Web page: http://www.econ.iastate.edu
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  1. McKinley L. Blackburn & David Neumark, 1991. "Omitted-Ability Bias and the Increase in the Return to Schooling," NBER Working Papers 3693, National Bureau of Economic Research, Inc.
  2. KOOPÂ , Gary & OSIEWALSKIÂ , Jacek & STEELÂ , Mark, 1995. "Bayesian Efficiency Analysis through Individual Effects : Hospital Cost Frontiers," CORE Discussion Papers 1995036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Adonis Yatchew, 1998. "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, American Economic Association, vol. 36(2), pages 669-721, June.
  4. Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December.
  5. Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 84-107.
  6. Gary Koop & Herman K. van Dijk, 1999. "Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach," Tinbergen Institute Discussion Papers 99-072/4, Tinbergen Institute.
  7. James Heckman & Edward Vytlacil, 2001. "Identifying The Role Of Cognitive Ability In Explaining The Level Of And Change In The Return To Schooling," The Review of Economics and Statistics, MIT Press, vol. 83(1), pages 1-12, February.
  8. Robinson, P M, 1988. "Semiparametric Econometrics: A Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(1), pages 35-51, January.
  9. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
  10. Li, Qi, et al, 2002. "Semiparametric Smooth Coefficient Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 412-22, July.
  11. John Cawley & Karen Conneely & James Heckman & Edward Vytlacil, 1996. "Cognitive Ability, Wages, and Meritocracy," NBER Working Papers 5645, National Bureau of Economic Research, Inc.
  12. John DiNardo & Justin L. Tobias, 2001. "Nonparametric Density and Regression Estimation," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 11-28, Fall.
  13. John Cawley & James Heckman & Edward Vytlacil, 1999. "On Policies To Reward The Value Added By Educators," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 720-727, November.
  14. Thomas C. Buchmueller & Robert G. Valletta, 1996. "The effect of health insurance on married female labor supply," Working Papers in Applied Economic Theory 96-09, Federal Reserve Bank of San Francisco.
  15. Chou, Y. J. & Staiger, Douglas, 2001. "Health insurance and female labor supply in Taiwan," Journal of Health Economics, Elsevier, vol. 20(2), pages 187-211, March.
  16. Tobias, Justin, 2001. "Are Returns to Schooling Concentrated Among the Most Able? A Semiparametric Analysis of the Ability-Earnings Relationships," Staff General Research Papers 12016, Iowa State University, Department of Economics.
  17. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
  18. Smith, M. & Kohn, R., . "Nonparametric Regression using Bayesian Variable Selection," Statistics Working Paper _009, Australian Graduate School of Management.
  19. Koop, Gary M & Tobias, Justin, 2004. "Learning About Heterogeneity in Returns to Schooling," Staff General Research Papers 12008, Iowa State University, Department of Economics.
  20. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September.
  21. Taber, Christopher R, 2001. "The Rising College Premium in the Eighties: Return to College or Return to Unobserved Ability?," Review of Economic Studies, Wiley Blackwell, vol. 68(3), pages 665-91, July.
  22. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, June.
  23. Shively, Thomas S. & Kohn, Robert, 1997. "A Bayesian approach to model selection in stochastic coefficient regression models and structural time series models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 39-52.
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