SMU SOE Online Seminar (Apr 9, 2021, 4pm-5.30pm): Near Optimal Estimation of Average Regression Functionals

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NEAR OPTIMAL ESTIMATION OF AVERAGE REGRESSION FUNCTIONALS

ABSTRACT This paper considers estimation of a continuous linear regression functional that can be written as a weighted population mean of observed outcome. A leading example is the population average treatment effect under unconfoundedness and overlap conditions, when the weight function is the product of binary treatment and inverse propensity score. We propose a new plug-in estimator of the functional when the weight function is approximated in series space. This estimator is near optimal in the sense that its mean square remainder error is controlled as small as possible in finite sample. We characterize its asymptotic distribution, allowing the number of basis functions k to grow proportionally to sample size n. We compare both finite sample and asymptotic performance of our estimator with doubly robust (DR) estimators. We find DR estimators often do not have materially smaller mean square remainder error in finite sample. DR estimators also do not improve the asymptotic performance when the ratio of k to n is smaller than 1. When the ratio is larger than 1, we propose a modified DR estimator to improve the asymptotic performance of our plug-in estimator. We apply our method to the work of Ferraz and Finan (2011) and conduct simulations to support theoretic findings. 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 8 April 2021.
		PRESENTER Chen Qiu Institute for Fiscal Studies University College London
		RESEARCH FIELDS Econometrics Causal Inference Machine Learning Applied Microeconometrics
		DATE: 9 April 2021 (Friday)
		TIME: 4.00pm - 5.30pm

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