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Regression with an Imputed Dependent Variable

Author

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  • Thomas Crossley

    (Institute for Fiscal Studies and University of Essex and European University Institute)

  • Peter Levell

    (Institute for Fiscal Studies and Institute for Fiscal Studies)

  • Stavros Poupakis

    (Institute for Fiscal Studies and University of Essex)

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 o?er 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).

Suggested Citation

  • Thomas Crossley & Peter Levell & Stavros Poupakis, 2019. "Regression with an Imputed Dependent Variable," IFS Working Papers W19/16, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:ifsewp:19/16
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    3. Peter Levell & David Sturrock, 2023. "Using Understanding Society to study intergenerational wealth mobility in the UK," Fiscal Studies, John Wiley & Sons, vol. 44(4), pages 417-432, December.

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