Template-Type:ReDIF-Paper 1.0  
Title: More on partitioned possibly restricted linear regression 
Author-Name: Werner, Hans Joachim 
Author-Name: Cemil Yapar
Classification-JEL:C20 
Keywords:Gauss-Markov model, singular model, perfect multicollinearity, partitioned linear regression, linear
	equality constraints, linear inequality constraints, constrained generalized least squares selections, oblique
	projectors, generalized inverses.
Abstract:This paper deals with the general partitioned linear regression model where the 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 equality or inequality restrictions. In
	particular, we are interested in the set of {\it generalized least squares (GLS) selections} for $\beta_2$. Inspired by
	Aigner and Balestra [1], as well as by Nurhonen and Puntanen [2], we also consider a specific reduced model and
	describe a scenario under which the set of GLS selections for $\beta_2$ under the reduced model equals the set of
	GLS selections for $\beta_2$ under the original full model. The results obtained in [2] and [1] for the unrestricted
	{\it standard} (full rank) regression model are reobtained as special cases. 
Length: pages
Creation-Date: 1994
Revision-Date: 
Handle: RePEc:bon:bonsfb:301
File-URL: http://www.wiwi.uni-bonn.de/bgsepapers/bonsfb/bonsfb301.pdf
File-Format: application/pdf
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