Under the least squares assumptions for the multiple regression problem (zero conditional mean for the error term, all Xi and Yi being i.i.d., all Xi and ui having finite fourth moments, no perfect multicollinearity), the OLS estimators for the slopes and intercept: a. have an approximate normal distribution for n > 50. b. are BLUE if the errors are homoskedastic. C. have a normal distribution in small samples as long as the errors are normally distributed. d. are unbiased and consistent. e. All of the above. f. None of the above.

Under the least squares assumptions for the multiple regression problem (zero conditional mean for the error term, all Xi and Yi being i.i.d., all Xi and ui having finite fourth moments, no perfect multicollinearity), the OLS estimators for the slopes and intercept: a. have an approximate normal distribution for n > 50. b. are BLUE if the errors are homoskedastic. C. have a normal distribution in small samples as long as the errors are normally distributed. d. are unbiased and consistent. e. All of the above. f. None of the above.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

Under the least squares assumptions for the multiple regression problem (zero
conditional mean for the error term, all Xi and Yi being i.i.d., all Xi and ui having finite
fourth moments, no perfect multicollinearity), the OLS estimators for the slopes and
intercept:
a.
have an approximate normal distribution for n > 50.
b.
are BLUE if the errors are homoskedastic.
C.
have a normal distribution in small samples as long as the errors are normally distributed.
d.
are unbiased and consistent.
e.
All of the above.
f.
None of the above.

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