Nonparametric Slope Estimators for Fixed-Effect Panel Data
In panel data the interest is often in slope estimation while taking account of the unobserved cross sectional heterogeneity. This paper proposes two nonparametric slope estimation where the unobserved effect is treated as fixed across cross section. The first estimator uses first-differencing transformation and the second estimator uses the mean deviation transformation. The asymptotic properties of the two estimators are established and the finite sample Monte Carlo properties of the two estimators are investigated allowing for systematic dependence between the cross-sectional effect and the independent variable. Simulation results suggest that the new nonparametric estimators perform better than the parametric counterparts. We also investigate the finite sample properties of the parametric within and first differencing estimators. A very common practice in estimating earning function is to assume earnings to be quadratic in age and tenure, but that might be misspecified. In this paper we estimate nonparametric slope of age and tenure on earnings using NLSY data and compare it to the parametric (quadratic) effect.
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