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|Title:||Pretest Estimators for the Two Sample Linear Statistical Model|
|Doctoral Committee Chair(s):||Judge, George,|
|Department / Program:||Economics|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Abstract:||In this dissertation, we will be concerned with the estimation of location parameters in the linear two sample regression problem with possibly a nonscalar error covariance matrix. This problem has been examined before by many authors where identical location parameters were assumed for the two samples, and the covariance matrix had the simplist type of heteroscedasticity. Frequently however for two samples of economic data both the location and scale parameters differ. The two sample heteroscedastic model has received considerable attention recently with the development of a two stage test: one for homoscedasticity followed by a main test for the location parameters. In this context we examine the sampling characteristics of the pretest estimator that makes use of this two stage test. The analytical risk will be derived, and the UNIFORM superiority of the 2 stage pretest estimator with respect to the GAUSS MARKOV estimator is shown. Finally, an extension of the two sample heteroscedastic model is the Zellner's seemingly unrelated regression model with contemporaneously correlated errors. We will examine the pretest estimator for Zellner's seemingly unrelated regression model, and show its risk characteristics.|
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1987.
|Date Available in IDEALS:||2014-12-16|