SMU SOE Job Talk Practice (December 7, 2022): Efficient Nonparametric Estimation of Generalized Panel Data Transformation Models with Fixed Effects

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TOPIC:

EFFICIENT NONPARAMETRIC ESTIMATION OF GENERALIZED PANEL DATA TRANSFORMATION MODELS WITH FIXED EFFECTS

ABSTRACT In this article, we consider a generalized panel data transformation model with fixed effects where the structural functions are assumed to be additive. Our model does not impose parametric assumptions on the transformation function, the structural function, or the distribution of the idiosyncratic error term. We propose a multiple-stage Local Maximum Likelihood Estimator (LMLE) for the structural functions. In the first step, we apply Logistic Series Estimator (LSE) to estimate conditional expectation and then apply matching method to obtain initial estimators. In the second step, instead of estimating the Cumulative Distribution Function (CDF) directly, we construct a new link function by introducing logistic function to ensure that the estimated probability is always within [0,1]. We use local- polynomial kernel regression to estimate the constructed link function. In the third step, given the estimators in Steps 1 and 2, we refine the initial estimator by the local-linear kernel regression. The greatest advantage is that all optimization problems are convex and thus overcome the computational hurdle for existing approaches to the generalized panel data transformation model. The final estimates of the additive terms achieve optimal one- dimensional convergence rate, asymptotic normality and oracle efficiency. The Monte Carlo simulations demonstrate that our new estimator performs well in finite samples.
		PRESENTER Ying Xia Singapore Management University
		RESEARCH FIELDS Econometric Theory Nonparametric Econometrics Panel Data
		DATE: 7 December 2022 (Wednesday)
		TIME: 2pm - 3.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903

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