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Imputation of continuous variables missing at random using the method of simulated scores

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  • Calzolari, Giorgio
  • Neri, Laura

Abstract

For multivariate datasets with missing values, we present a procedure of statistical inference and state its "optimal" properties. Two main assumptions are needed: (1) data are missing at random (MAR); (2) the data generating process is a multivariate normal linear regression. Disentangling the problem of convergence of the iterative estimation/imputation procedure, we show that the estimator is a "method of simulated scores" (a particular case of McFadden's "method of simulated moments"); thus the estimator is equivalent to maximum likelihood if the number of replications is conveniently large, and the whole procedure can be considered an optimal parametric technique for imputation of missing data.

Suggested Citation

  • Calzolari, Giorgio & Neri, Laura, 2002. "Imputation of continuous variables missing at random using the method of simulated scores," MPRA Paper 22986, University Library of Munich, Germany, revised 2002.
    • Handle: RePEc:pra:mprapa:22986
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    References listed on IDEAS

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    1. Vassilis A. Hajivassiliou & Daniel McFadden, 1990. "The Method of Simulated Scores for the Estimation of LDV Models with an Application to External Debt Crisis," Cowles Foundation Discussion Papers 967, Cowles Foundation for Research in Economics, Yale University.
    2. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    3. Paul Kofman & Ian G. Sharpe, 2003. "Using Multiple Imputation in the Analysis of Incomplete Observations in Finance," Journal of Financial Econometrics, Oxford University Press, vol. 1(2), pages 216-249.
    4. Foschi, Paolo & Belsley, David A. & Kontoghiorghes, Erricos J., 2003. "A comparative study of algorithms for solving seemingly unrelated regressions models," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 3-35, October.
    5. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
    6. Pollock, D. S. G., 2003. "Recursive estimation in econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 37-75, October.
    7. Belsley, David A. & John Kontoghiorghes, Erricos, 2005. "Second Special issue on Computational Econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 283-285, April.
    8. Vassilis A. Hajivassiliou, 1991. "Simulation Estimation Methods for Limited Dependent Variable Models," Cowles Foundation Discussion Papers 1007, Cowles Foundation for Research in Economics, Yale University.
    9. Bianchi, Carlo & Calzolari, Giorgio & Corsi, Paolo, 1978. "A Program for Stochastic Simulation of Econometric Models," Econometrica, Econometric Society, vol. 46(1), pages 235-236, January.
    10. Mariano,Roberto & Schuermann,Til & Weeks,Melvyn J. (ed.), 2000. "Simulation-based Inference in Econometrics," Cambridge Books, Cambridge University Press, number 9780521591126, January.
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    Cited by:

    1. Doretti, Marco, 2012. "Modelli di scoring per il rischio paese [Scoring models for country risk]," MPRA Paper 38898, University Library of Munich, Germany.

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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