SMU SOE Lee Kong Chian Seminar in Econometrics (Feb 21, 2022, 9.00am-10.30am): Spatial Correlation Robust Inference

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ABSTRACT We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar `estimator plus and minus a standard error times a critical value' form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given `worst-case' spatial covariance model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in large samples whenever the spatial correlation is weak, i.e., with average pairwise correlations that vanish as the sample size gets large. We also provide results on correct coverage in a restricted but nonparametric class of strong spatial correlations, as well as on the efficiency of the method. In a design calibrated to match economic activity in U.S. states the method outperforms previous suggestions for spatially robust inference about the population mean. Keywords: Confidence interval, HAR, HAC, Random field JEL Codes: C12, C20 Click here to view the paper. 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 18 Feb 2022.
		PRESENTER Ulrich K. Müller Princeton University
		RESEARCH FIELDS Econometrics
		DATE: 21 February 2022 (Monday)
		TIME: 9.00am - 10.30am