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Nonparametric Estimation of Marginal Effects in Regression-spline Random Effects Models

Author

Listed:
  • Aman Ullah

    (Department of Economics, University of California Riverside)

  • Shujie Ma

    (Department of Statistics, University of California Riverside)

  • Jeffrey Racine

    (Department of Economics, McMaster University)

Abstract

We consider a B-spline regression approach towards nonparametric modelling of a random effects (error component) model. We focus our attention on the estimation of marginal effects (derivatives) and their asymptotic properties. 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

  • Aman Ullah & Shujie Ma & Jeffrey Racine, 2019. "Nonparametric Estimation of Marginal Effects in Regression-spline Random Effects Models," Working Papers 201920, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:201920
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    File URL: https://economics.ucr.edu/repec/ucr/wpaper/201920.pdf
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    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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