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Regression with an imputed dependent variable

Crossley, Thomas F; Levell, Peter; Poupakis, Stavros; (2022) Regression with an imputed dependent variable. Journal of Applied Econometrics 10.1002/jae.2921. (In press). Green open access

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Abstract

Researchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent variable that are common to two surveys to impute the dependent variable into the data set containing the independent variable. We show that commonly employed regression or matching-based imputation procedures lead to inconsistent estimates. We offer a consistent and easily implemented two-step estimator, “rescaled regression prediction.” We derive the correct asymptotic standard errors for this estimator and demonstrate its relationship to alternative approaches. We illustrate with empirical examples using data from the US Consumer Expenditure Survey (CE) and the Panel Study of Income Dynamics (PSID).

Type: Article
Title: Regression with an imputed dependent variable
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/jae.2921
Publisher version: https://doi.org/10.1002/jae.2921
Language: English
Additional information: © 2022 Institute for Fiscal Studies and The Authors. Journal of Applied Econometrics published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: consumption, imputation, measurement error
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10156658
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