Nonparametric estimation of the marginal effect in fixed-effect panel data models

Yoonseok Lee, Debasri Mukherjee, Aman Ullah

Research output: Contribution to journalArticle

Abstract

This paper considers multivariate local linear least squares estimation of panel data models when fixed effects are present. One-step estimation of the local marginal effect is of prime interest. A within-group nonparametric estimator is developed, where the fixed effects are eliminated by subtracting individual-specific locally weighted time average, i.e., 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, nonparametric estimators based on the conventional (global) within-transformation or first difference result in estimators which are biased, even in 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 US state-level panel data.

Original languageEnglish (US)
Pages (from-to)53-67
Number of pages15
JournalJournal of Multivariate Analysis
Volume171
DOIs
StatePublished - May 1 2019

Fingerprint

Fixed Effects
Panel Data
Nonparametric Estimation
Data Model
Data structures
Nonparametric Estimator
Nitrogen oxides
Estimator
Local Linear Estimator
Linear Estimation
Linear Least Squares
Least Squares Estimation
Time-average
Nitrogen
Biased
Oxides
Curve
Nonparametric estimation
Fixed effects
Marginal effects

Keywords

  • Environmental Kuznets curve
  • Fixed effects
  • Local-within-transformation
  • Multivariate local linear least squares
  • Nonparametric estimation
  • Panel data

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Cite this

Nonparametric estimation of the marginal effect in fixed-effect panel data models. / Lee, Yoonseok; Mukherjee, Debasri; Ullah, Aman.

In: Journal of Multivariate Analysis, Vol. 171, 01.05.2019, p. 53-67.

Research output: Contribution to journalArticle

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