Asymptotics of Multivariate Regression with Consecutively Added Dependent Varibles
AbstractWe consider multivariate regression where new dependent variables are consecutively added during the experiment (or in time).So, viewed at the end of the experiment, the number of observations decreases with each added variable. The explanatory variables are observed throughout.In a previous paper we determined the least squares and maximum likelihood estimators for the parameters in this model.In this paper we discuss the estimation technique of iterative least squares to calculate the maximum likelihood estimates and we prove the consistency of the estimators in each iteration.Moreover, we introduce a general class of estimators for the regression parameters based on arbitrary starting estimators for the covariance matrix.We prove the consistency of these new estimators and - for sake of completeness - of the previously obtained least squares and maximum likelihood estimators as well.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2004-77.
Date of creation: 2004
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added dependent variables; consistency; iterative weighted least squares; maximum likelihood; monotone missing data;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
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- Magnus, Jan R., 1978. "Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix," Journal of Econometrics, Elsevier, vol. 7(3), pages 281-312, April.
- Magnus, J.R., 1978. "Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix," Open Access publications from Tilburg University urn:nbn:nl:ui:12-153204, Tilburg University.
- Raats, V.M. & Genugten, B.B. van der & Moors, J.J.A., 2002. "Multivariate Regression with Monotone Missing Observation of the Dependent Variables," Discussion Paper 2002-63, Tilburg University, Center for Economic Research.
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