a) List and discuss the assumptions that make the Ordinary Least Squares (OLS) the Best Linear Unbiased Estimator (BLUE). b) Derive the OLS estimator and variance and discuss where each assumption is needed for the derivation of the two parameters. c) Discuss the properties of linearity, unbiasedness, and efficiency, and what assumption you need for each of these properties to hold.
Consider the following model:
? = ?? + ?,
known as the Classical Linear Regression Model (CLRM), where y is the
dependent variable, X is the set of independent variables, ? is the vector of
parameters to be estimated and ? is the error term.
a) List and discuss the assumptions that make the Ordinary Least Squares
(OLS) the Best Linear Unbiased Estimator (BLUE).
b) Derive the OLS estimator and variance and discuss where each
assumption is needed for the derivation of the two parameters.
c) Discuss the properties of linearity, unbiasedness, and efficiency, and
what assumption you need for each of these properties to hold.
d) Present and discuss the R2 and the adjusted R2 Discuss pros and cons of
each of the two statistics.
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