what happens if assumptions are broken
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9 Terms
1
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no autocorrelation between variables
standard error will be under estimated
2
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no multicollinearity
OLS can’t be computed
3
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no covariance between x and error
wil become biased and inconsistent
4
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homoscedasicity
OLS is no longer best
5
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linear in parameters
no longer a linear model
6
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variance of x is positive
coefficient can’t be computed
7
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x is fixed in repeated samples
x becomes random but everything works
8
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expected error term is 0
bata 1 becomes biased
9
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number of observations is greater than batas
OLS can’t be calculated
Note 
Flashcards (29)