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.
|Place of Publication||Tilburg|
|Number of pages||32|
|Publication status||Published - 2002|
|Name||CentER Discussion Paper|
- least squares
- maximum likelihood
- multivariate regression
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.