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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 an easily-implemented correction and correct asymptotic standard errors. We illustrate these with Monte Carlo experiments and empirical examples using data from the US Consumer Expenditure Survey (CE) and the Panel Study of Income Dynamics (PSID).
Authors
Research Fellow University of Michigan
Tom is a Research Fellow at IFS, a Research Professor for the Institute for Social Research at the University of Michigan.
Associate Director
Peter joined in 2009. He has published several papers on the microeconomics of household spending and labour supply decisions over the life-cycle.
PhD Student University of Essex
Working Paper details
- DOI
- 10.1920/wp.ifs.2019.1619
- Publisher
- The IFS
Suggested citation
T, Crossley and P, Levell and S, Poupakis. (2019). Regression with an Imputed Dependent Variable. London: The IFS. Available at: https://ifs.org.uk/publications/regression-imputed-dependent-variable-0 (accessed: 19 March 2024).
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