SMU SOE Online Seminar (Sep 13, 2021, 9.00am-10.30am): Two-Stage Maximum Score Estimator

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TOPIC:

TWO-STAGE MAXIMUM SCORE ESTIMATOR

ABSTRACT This paper considers the asymptotic theory of a semiparametric M-estimator that is generally applicable to models that satisfy a monotonicity condition in one or several parametric indexes. We call this estimator the two-stage maximum score (TSMS) estimator, since our estimator involves a first-stage nonparametric regression when applied to the binary choice model of Manski (1975, 1985). We characterize the asymptotic distribution of the TSMS estimator, which features phase transitions depending on the dimension of the first-stage estimation. We show that the TSMS estimator is asymptotically equivalent to the smoothed maximum-score estimator (Horowitz, 1992) when the dimension of the first-step estimation is relatively low, while still achieving partial rate acceleration relative to the cubic-root rate when the dimension is not too high. Effectively, the first-stage nonparametric estimator serves as an imperfect smoothing function on a non-smooth criterion function, leading to the pivotality of the first-stage estimation error with respect to the second-stage convergence rate and asymptotic distribution. Click here to view the CV.		This seminar will be held virtually via Zoom. A confirmation email with the Zoom details will be sent to the registered email by 10 September 2021.
		PRESENTER Wayne Yuan Gao University of Pennsylvania
		RESEARCH FIELDS Econometrics Microeconomic Theory Economics of Networks
		DATE: 13 September 2021 (Monday)
		TIME: 9.00am - 10.30am

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