**Author**: Werner, Hans Joachim, and Cemil Yapar

**Title**: A BLUE Decomposition in the General Linear Regression Model

**Abstract**: In this note we consider the {\it general} linear regression model
$(y,\ X\beta,\ V\mid\ R_2\beta_2=r)$ where the block partitioned
regressor matrix $X=\pmatrix{X_1 & X_2\cr}$ may be deficient in
column rank, the dispersion matrix $V$ is possibly singular,
$\beta^t=\pmatrix{\beta_1^t & \beta_2^t\cr}$ - being partitioned
according to $X$ - is the vector of unknown regression coefficients,
and $\beta_2$ is possibly subject to consistent linear constraints
$R_2\beta_2=r$. Of much interest to us is the {\it traditional} BLUE
{\it (best linear unbiased estimator)} of $X\beta$. We show how this
traditional BLUE can obtained from the traditional BLUE of
$X_1\beta_1$ in the related model $(y,\ X_1\beta_1,\ V)$. Some
properties of the dispersion matrix of the traditional BLUE of
$X\beta$ are also given.

**Keywords**: Linear regression model, rank deficiency, linear
constraints, traditional BLUE, dispersion, g-inversion.

**JEL-Classification-Number**: C20

**Creation-Date**: 1995

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