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Multivariate Regression with Monotone Missing Observation of the Dependent Variables


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

    (Tilburg University, Center for Economic Research)

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    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.

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    Bibliographic Info

    Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2002-63.

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    Date of creation: 2002
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    Handle: RePEc:dgr:kubcen:200263

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    Related research

    Keywords: testing; sampling; least squares; maximum likelihood; multivariate regression;

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    Cited by:
    1. Raats, V.M., 2004. "Approximations of the Generalized Wilks' Distribution," Discussion Paper 2004-85, Tilburg University, Center for Economic Research.
    2. Raats, V.M. & Genugten, B.B. van der & Moors, J.J.A., 2004. "Asymptotics of Multivariate Regression with Consecutively Added Dependent Varibles," Discussion Paper 2004-77, Tilburg University, Center for Economic Research.
    3. Raats, V.M. & Moors, J.J.A. & Genugten, B.B. van der, 2004. "A Mixed Model for Double Checking Fallible Auditors," Discussion Paper 2004-82, Tilburg University, Center for Economic Research.


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