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Sequential Regression Multiple Imputation for Incomplete Multivariate Data using Markov Chain Monte Carlo

Author

Listed:
  • Miguel Lacerda
  • Cally Ardington

    (SALDRU, School of Economics, University of Cape TownAuthor-Email:)

  • Murray Leibbrandt

    (SALDRU, School of Economics, University of Cape Town)

Abstract

This paper discusses the theoretical background to handling missing data in a multivariate context. Earlier methods for dealing with item non-response are reviewed, followed by an examination of some of the more modern methods and, in particular, multiple imputation. One such technique, known as sequential regression multivariate imputation, which employs a Markov chain Monte Carlo algorithm is described and implemented. It is demonstrated that distributional convergence is rapid and only a few imputations are necessary in order to produce accurate point estimates and preserve multivariate relationships, whilst adequately accounting for the uncertainty introduced by the imputation procedure. It is further shown that lower fractions of missing data and the inclusion of relevant covariates in the imputation model are desirable in terms of bias reduction.

Suggested Citation

  • Miguel Lacerda & Cally Ardington & Murray Leibbrandt, 2007. "Sequential Regression Multiple Imputation for Incomplete Multivariate Data using Markov Chain Monte Carlo," SALDRU Working Papers 13, Southern Africa Labour and Development Research Unit, University of Cape Town.
    • Handle: RePEc:ldr:wpaper:13
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    References listed on IDEAS

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    1. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
    2. Javaras, Kristin N. & Van Dyk, David A., 2003. "Multiple Imputation for Incomplete Data With Semicontinuous Variables," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 703-715, January.
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    Cited by:

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    2. Derek Yu, 2013. "Some factors influencing the comparability and reliability of poverty estimates across household surveys," Working Papers 03/2013, Stellenbosch University, Department of Economics.
    3. David McLennan & Michael Noble & Gemma Wright & Helen Barnes & Faith Masekesa, 2021. "Exploring the quality of income data in two African household surveys for the purpose of tax-benefit microsimulation modelling: Imputing employment income in Tanzania and Zambia," WIDER Working Paper Series wp-2021-134, World Institute for Development Economic Research (UNU-WIDER).

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