The McGraw-Hill Series Economics essentials of economics brue, McConnell, and Flynn Essentials of Economics



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Part Two
Relaxing the Assumptions of the Classical Model
319
7
This is not for lack of trying. See A. K. Bera and C. M. Jarque, “Efficient Tests for Normality,
Homoscedasticity and Serial Independence of Regression Residuals: Monte Carlo Evidence,”
Economic Letters,
vol. 7, 1981, pp. 313–318.
A cautionary note is in order: As noted earlier, satisfactory answers to all the problems
arising out of the violation of the assumptions of the classical linear regression model
(CLRM) do not exist. Moreover, there may be more than one solution to a particular prob-
lem, and often it is not clear which method is best. Besides, in a particular application more
than one violation of the CLRM may be involved. Thus, specification bias, multicollinear-
ity, and heteroscedasticity may coexist in an application, and there is no single omnipotent
test that will solve all the problems simultaneously.
7
Furthermore, a particular test that was
popular at one time may not be in vogue later because somebody found a flaw in the earlier
test. But this is how science progresses. Econometrics is no exception.
guj75772_ch10.qxd 12/08/2008 02:44 PM Page 319


320
Chapter
10
Multicollinearity:
What Happens
If the Regressors
Are Correlated?
1
Edward E. Leamer, “Model Choice and Specification Analysis,” in Zvi Griliches and Michael D. Intrili-
gator, eds., 
Handbook of Econometrics
, vol. I, North Holland Publishing Company, Amsterdam, 1983,
pp. 300–301.
There is no pair of words that is more misused both in econometrics texts and in the applied
literature than the pair “multi-collinearity problem.” That many of our explanatory variables are
highly collinear is a fact of life. And it is completely clear that there are experimental designs

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