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Semiparametric Bayesian Inference in Smooth Coefficient Models

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  • Koop, Gary M
  • Tobias, Justin

Abstract

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.

Suggested Citation

  • Koop, Gary M & Tobias, Justin, 2006. "Semiparametric Bayesian Inference in Smooth Coefficient Models," Staff General Research Papers Archive 12202, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:12202
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    References listed on IDEAS

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    1. Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 77-105.
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    6. Koop, Gary M & Tobias, Justin, 2004. "Learning About Heterogeneity in Returns to Schooling," Staff General Research Papers Archive 12008, Iowa State University, Department of Economics.
    7. Christopher R. Taber, 2001. "The Rising College Premium in the Eighties: Return to College or Return to Unobserved Ability?," Review of Economic Studies, Oxford University Press, vol. 68(3), pages 665-691.
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    Cited by:

    1. Dorfman, Jeffrey H. & Karali, Berna, 2010. "Do Farmers Hedge Optimally or by Habit? A Bayesian Partial-Adjustment Model of Farmer Hedging," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 42(04), pages 791-803, November.
    2. Karali, Berna & Dorfman, Jeffrey H. & Thurman, Walter N., 2008. "Do Inventory and Time-to-Delivery Effects Vary Across Futures Contracts? Insights from a Smoothed Bayesian Estimator," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6084, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Dorfman, Jeffrey H. & Patridge, Mark D. & Galloway, Hamilton, 2008. "Are High-Tech Employment and Natural Amenities Linked?: Answers from a Smoothed Bayesian Spatial Model," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6459, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Suzanna-Maria Paleologou, 2016. "The long-run tendency of government expenditure: a semi-parametric modelling approach," Empirical Economics, Springer, vol. 50(3), pages 753-776, May.
    5. Agee, Mark D. & Atkinson, Scott E. & Crocker, Thomas D. & Williams, Jonathan W., 2014. "Non-separable pollution control: Implications for a CO2 emissions cap and trade system," Resource and Energy Economics, Elsevier, vol. 36(1), pages 64-82.
    6. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.
    7. Scott E. Atkinson & Jeffrey H. Dorfman, 2009. "Feasible estimation of firm-specific allocative inefficiency through Bayesian numerical methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 675-697.
    8. Huang, Ho-Chuan (River) & Lin, Shu-Chin, 2008. "Smooth-time-varying Okun's coefficients," Economic Modelling, Elsevier, vol. 25(2), pages 363-375, March.
    9. Bacolod, Marigee P. & Tobias, Justin L., 2006. "Schools, school quality and achievement growth: Evidence from the Philippines," Economics of Education Review, Elsevier, vol. 25(6), pages 619-632, December.
    10. Amin Mugera & Michael Langemeier & Allen Featherstone, 2012. "Labor productivity convergence in the Kansas farm sector: a three-stage procedure using data envelopment analysis and semiparametric regression analysis," Journal of Productivity Analysis, Springer, vol. 38(1), pages 63-79, August.
    11. Zheng, Xiaoyong, 2008. "Semiparametric Bayesian estimation of mixed count regression models," Economics Letters, Elsevier, vol. 100(3), pages 435-438, September.

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