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Multiple Imputations for Linear Regression Models

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  • Brownstone, David

Abstract

Rubin (1987) has proposed multiple imputations as a general method for estimation ion the presence of missing data. Rubin's results only strictly apply to Bayesian models, but Schenker and Welsh (1988) directly prove the consistency of multiple imputations inferences when there are missing values of the dependent variable in linear regression models. This paper extends and modifies Schenker and Welsh's theorems to give conditions where multiple imputations yield consistent inferences for both ignorable and nonignorable missing data in exogenous variables. One key condition is that the imputed values must have the same conditional first and second moments as the true values. Monte Carlo studies show that the multiple imputation covariance estimates are accurate for realistic sample sizes. They also support the applications of multiple imputations in Brownstone and Valletta (1991), where the multiple imputations estimates substantially changed the qualitative conclusions implied by the model.

Suggested Citation

  • Brownstone, David, 1991. "Multiple Imputations for Linear Regression Models," University of California Transportation Center, Working Papers qt5rv0265r, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt5rv0265r
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    Cited by:

    1. Kaestner, Robert & Joyce, Theodore & Wehbeh, Hassan, 1996. "The Effect of Maternal Drug Use on Birth Weight: Measurement Error in Binary Variables," Economic Inquiry, Western Economic Association International, vol. 34(4), pages 617-629, October.
    2. Brownstone, David & Valletta, Robert G, 1996. "Modeling Earnings Measurement Error: A Multiple Imputation Approach," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 705-717, November.
    3. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    4. Artés, Joaquín, 2009. "Long-run versus short-run decisions: R&D and market structure in Spanish firms," Research Policy, Elsevier, vol. 38(1), pages 120-132, February.
    5. Guevara, C. Angelo & Tang, Yue & Gao, Song, 2018. "The initial condition problem with complete history dependency in learning models for travel choices," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 850-861.

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    Social and Behavioral Sciences;

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