SMU SOE Seminar (Nov 16, 2018): Uniform Nonparametric Inference for Time Series

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TOPIC:

UNIFORM NONPARAMETRIC INFERENCE FOR TIME SERIES

ABSTRACT This paper provides the first result for the uniform inference based on nonparametric series estimators in a general time-series setting. We develop a strong approximation theory for sample averages of mixingales with dimensions growing with the sample size. We use this result to justify the asymptotic validity of a uniform confidence band for series estimators and show that it can also be used to conduct nonparametric specification test for conditional moment restrictions. New results on the validity of high-dimensional heteroskedasticity and autocorrelation consistent (HAC) estimators are established for making feasible inference. Further extensions include time-series inference theories for intersection bounds and convex sieve M-estimators, which permit applications in partially identified models and nonparametric conditional quantile estimation, respectively. The proposed methods are broadly useful for conditional forecast evaluation, risk management, empirical microstructure, asset pricing and general dynamic stochastic equilibrium models. We demonstrate the empirical relevance of the proposed method by studying the Mortensen-Pissarides search and matching model, and shed new light on the unemployment volatility puzzle from an econometric perspective. Keywords: Martingale difference; Mixingale; Series estimation; Specification test, Strong approximation; Uniform inference; Unemployment. Click here to view the paper. Click here to view the CV.
		PRESENTER Jia Li Duke University
		RESEARCH FIELDS Econometrics Financial Economics
		DATE: 16 November 2018 (Friday)
		TIME: 4pm - 5.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903

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