Nonparametric Estimation of Marginal Effects in Regression-spline Random Effects Models
Shujie Ma () and
Jeffrey Racine ()
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Shujie Ma: Department of Statistics, University of California Riverside
No 201920, Working Papers from University of California at Riverside, Department of Economics
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.
JEL-codes: C14 C23 (search for similar items in EconPapers)
Pages: 47 Pages
New Economics Papers: this item is included in nep-ecm, nep-his and nep-ore
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https://economics.ucr.edu/repec/ucr/wpaper/201920.pdf First version, 2019 (application/pdf)
Journal Article: Nonparametric estimation of marginal effects in regression-spline random effects models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201920
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