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Speaker: Jing Wang

Affiliation: Department of Mathematics, Statistics, and Computer Science, University of Illinois Chicago

Title: Semiparametric estimation of non-ignorable missingness with refreshment sample

Abstract: Missing data is one of the major methodological problems in longitudinal studies. It not only reduces the sample size, but also can result in biased estimation and inference. It is crucial to correctly understand the missing mechanism and appropriately incorporate it into the estimation and inference procedures. Traditional methods, such as the complete case analysis and imputation methods, are designed to deal with missing data under unverifiable assumptions of MCAR and MAR. Our focus is on the identification and estimation of attrition (missing) parameters under the non-ignorable missingness assumption using the refreshment sample in two-wave panel data. We propose a full-likelihood parametric approach when the joint distribution of the two-wave data belongs to a given family. When one is unable to specify the joint distribution, we propose a semi-parametric method to estimate the attrition parameters by marginal density estimates with the help of two constraints from Hirano(2001) and the additional information provided by the refreshment sample. We derive asymptotic properties of the semi-parametric estimators and illustrate their performance with simulations. Inference based on bootstrapping is proposed and verified through simulations. A real data application is attempted in the Netherlands Mobility Panel. This is a joint work with Dr. Lan Xue and our graduate students.

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https://cwm.zoom.us/j/94709767190?pwd=Mi9YSEhnaE4za2JhSmFJaHh0TFduZz09 

Meeting ID: 947 0976 7190

Passcode: 2022

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GuanNan Wang

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