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The Method of Simulated Scores for Estimating Multinormal Regression Models with Missing Values

Given a set of continuous variables with missing data, we prove in this paper that the iterative application of a simple “least-squares estimation/multivariate normal simulation” procedure produces an efficient parameters estimator. There are two main assumptions behind our proof: (1) the missing data mechanism is ignorable; (2) the data generating process is a multivariate normal linear regression. Disentangling the iterative procedure and its convergence conditions, we show that the estimator is a “method of simulated scores” (a particular case of McFadden’s “method of simulated moments”), thus equivalent to maximum likelihood if the number of replications is conveniently large. We thus provide a non-Bayesian re-interpretation of the estimation/simulation problem. The computational procedure is obtained introducing a simple modification into existing algorithms. Its software implementation is straightforward (few simple statements in any programming language) and easily applicable to datasets with large number of variables.

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Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" in its series Econometrics Working Papers Archive with number wp2010_01.

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Length: 29
Date of creation: Jan 2010
Date of revision:
Handle: RePEc:fir:econom:wp2010_01
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  1. 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.
  2. Bianchi, Carlo & Calzolari, Giorgio & Corsi, Paolo, 1978. "A Program for Stochastic Simulation of Econometric Models," Econometrica, Econometric Society, vol. 46(1), pages 235-36, January.
  3. 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.
  4. Paul Kofman & Ian G. Sharpe, 2003. "Using Multiple Imputation in the Analysis of Incomplete Observations in Finance," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 1(2), pages 216-249.
  5. Pollock, D. S. G., 2003. "Recursive estimation in econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 37-75, October.
  6. 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.
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