Multivariate Regression with Monotone Missing Observation of the Dependent Variables

V.M. Raats, B.B. van der Genugten, J.J.A. Moors

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Abstract

Multivariate regression is discussed, where the observations of the dependent variables are (monotone) missing completely at random; the explanatory variables are assumed to be completely observed.We discuss OLS-, GLS- and a certain form of E(stimated) GLS-estimation.It turns out that (E)GLS-estimation uses the preceding dependent variables in a well-structured way.In case of normality, ML-estimation coincides with (E)GLS-estimation.We include (sets of) MANOVA-tables enabling us to perform exact tests on the coecients based on a (new) generalized Wilks' distribution.Only the very special case of the constant as sole explanatory variable has been treated in the literature so far: our model incorporates this missing data problem.
Original languageEnglish
Place of PublicationTilburg
PublisherEconometrics
Number of pages32
Volume2002-63
Publication statusPublished - 2002

Publication series

NameCentER Discussion Paper
Volume2002-63

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Keywords

  • testing
  • sampling
  • least squares
  • maximum likelihood
  • multivariate regression

Cite this

Raats, V. M., van der Genugten, B. B., & Moors, J. J. A. (2002). Multivariate Regression with Monotone Missing Observation of the Dependent Variables. (CentER Discussion Paper; Vol. 2002-63). Econometrics.