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Nonparametric Regression-Spline Random Effects Models

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  • Shujie Ma
  • Jeffrey S. Racine
  • Aman Ullah

Abstract

We consider a B-spline regression approach towards efficient nonparametric modelling of a random effects (error component) model. Theoretical underpinnings are provided, finite-sample performance is evaluated via Monte Carlo simulation, and an application that examines the contribution of different types of public infrastructure on private production is investigated using panel data comprising the 48 contiguous states in the US over the period 1970-1986.

Suggested Citation

  • Shujie Ma & Jeffrey S. Racine & Aman Ullah, 2015. "Nonparametric Regression-Spline Random Effects Models," Department of Economics Working Papers 2015-10, McMaster University.
    • Handle: RePEc:mcm:deptwp:2015-10
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    File URL: http://socserv.mcmaster.ca/econ/rsrch/papers/archive/2015-10.pdf
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    References listed on IDEAS

    as
    1. Martins-Filho, Carlos & Yao, Feng, 2009. "Nonparametric regression estimation with general parametric error covariance," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 309-333, March.
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    3. Peter Hall & Qi Li & Jeffrey S. Racine, 2007. "Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 784-789, November.
    4. Su, Liangjun & Ullah, Aman, 2007. "More efficient estimation of nonparametric panel data models with random effects," Economics Letters, Elsevier, vol. 96(3), pages 375-380, September.
    5. Henderson, Daniel J. & Ullah, Aman, 2005. "A nonparametric random effects estimator," Economics Letters, Elsevier, vol. 88(3), pages 403-407, September.
    6. Harry Haupt & Kathrin Kagerer & Winfried J. Steiner, 2014. "Smooth Quantile‐Based Modeling Of Brand Sales, Price And Promotional Effects From Retail Scanner Panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(6), pages 1007-1028, September.
    7. Shujie Ma & Jeffrey S. Racine & Lijian Yang, 2015. "Spline Regression in the Presence of Categorical Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 705-717, August.
    8. Henderson, Daniel J. & Carroll, Raymond J. & Li, Qi, 2008. "Nonparametric estimation and testing of fixed effects panel data models," Journal of Econometrics, Elsevier, vol. 144(1), pages 257-275, May.
    9. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
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    11. Baltagi, Badi H & Pinnoi, Nat, 1995. "Public Capital Stock and State Productivity Growth: Further Evidence from an Error Components Model," Empirical Economics, Springer, vol. 20(2), pages 351-359.
    12. Liangjun Su & Aman Ullah & Yun Wang, 2013. "Nonparametric regression estimation with general parametric error covariance: a more efficient two-step estimator," Empirical Economics, Springer, vol. 45(2), pages 1009-1024, October.
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    Cited by:

    1. Christopher F. Parmeter & Jeffrey S. Racine, 2018. "Nonparametric Estimation and Inference for Panel Data Models," Department of Economics Working Papers 2018-02, McMaster University.
    2. Vafadarnikjoo, Amin & Chalvatzis, Konstantinos & Botelho, Tiago & Bamford, David, 2023. "A stratified decision-making model for long-term planning: Application in flood risk management in Scotland," Omega, Elsevier, vol. 116(C).
    3. Serfas, D., 2018. "an ex-post econometric analysis of the abolishment of the canadian wheat board," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277286, International Association of Agricultural Economists.

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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