Nonparametric Estimation of the Marginal Effect in Fixed-Effect Panel Data Models

Aman Ullah, Yoonseok Lee and Debasri Mukherjee ()

No 201901, Working Papers from University of California at Riverside, Department of Economics

Abstract: This paper considers multivariate local linear least squares estimation of panel data models when fixed effects present. One step estimation of the local marginal effect is of the main interest. A within-group type nonparametric estimator is developed, where the fixed effects are eliminated by subtracting individual-specific locally weighted time average (i.e., using the local within transformation). It is shown that the local-within-transformation-based estimator satisfies the standard properties of the local linear estimator. In comparison, the nonparametric estimators based on the conventional (i.e., global) within transformation or first difference result in biased estimators, where the bias does not degenerate even with large samples. The new estimator is used to examine the nonlinear relationship between income and nitrogen-oxide level (i.e., the environmental Kuznets curve) based on the U.S. state-level panel data.

Keywords: Nonparametric estimation; panel data; fixed effects; multivariate; local linear least squares; local within transformation; environmental Kuznets curve. (search for similar items in EconPapers)
JEL-codes: C14 C23 (search for similar items in EconPapers)
Date: 2018-09
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (8)

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https://economics.ucr.edu/repec/ucr/wpaper/201901.pdf First version, 2018 (application/pdf)

Related works:
Journal Article: Nonparametric estimation of the marginal effect in fixed-effect panel data models (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201901

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