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

Giorgio Calzolari and Laura Neri

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Simulates scores; missing data; estimation/imputation; structural form; reduced form (search for similar items in EconPapers)
JEL-codes: C15 (search for similar items in EconPapers)
Date: 2002, Revised 2002
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Citations: View citations in EconPapers (1)

Published in Compstat 2002, Proceedings in Computational Statistics, 15th Symposium held in Berlin Ed. by W. Haerdle and B. Roenz. Heidelberg: Physika Verlag (2002): pp. 389-394

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Related works:
Working Paper: The Method of Simulated Scores for Estimating Multinormal Regression Models with Missing Values (2010)
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