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 language | English |
|---|---|
| Place of Publication | Tilburg |
| Publisher | Econometrics |
| Number of pages | 32 |
| Volume | 2002-63 |
| Publication status | Published - 2002 |
Publication series
| Name | CentER Discussion Paper |
|---|---|
| Volume | 2002-63 |
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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.