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Nonparametric estimation of marginal effects in regression-spline random effects models

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  • Shujie Ma
  • Jeffrey S. Racine
  • Aman Ullah

Abstract

We consider a B-spline regression approach toward nonparametric modeling 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 United States over the period 1970–1986.

Suggested Citation

  • Shujie Ma & Jeffrey S. Racine & Aman Ullah, 2020. "Nonparametric estimation of marginal effects in regression-spline random effects models," Econometric Reviews, Taylor & Francis Journals, vol. 39(8), pages 792-825, September.
    • Handle: RePEc:taf:emetrv:v:39:y:2020:i:8:p:792-825
    DOI: 10.1080/07474938.2020.1772569
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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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