Question 1 Consider the following model: y = XB + E, known as the Classical Linear Regression Model (CLRM), where y is the dependent variable, X is the set of independent variables, ß is the vector of parameters to be estimated and & is the error term. a) List and discuss the assumptions you need for the Ordinary Least Squares (OLS) to be a Best Linear Unbiased Estimator (BLUE). b) Derive the OLS estimator, discussing where the assumptions are needed for the derivation. c) Discuss the properties of linearity, unbiasedness and efficiency, discussing what assumption you need for each of these properties to hold.
Question 1 Consider the following model: y = XB + E, known as the Classical Linear Regression Model (CLRM), where y is the dependent variable, X is the set of independent variables, ß is the vector of parameters to be estimated and & is the error term. a) List and discuss the assumptions you need for the Ordinary Least Squares (OLS) to be a Best Linear Unbiased Estimator (BLUE). b) Derive the OLS estimator, discussing where the assumptions are needed for the derivation. c) Discuss the properties of linearity, unbiasedness and efficiency, discussing what assumption you need for each of these properties to hold.
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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Transcribed Image Text:Question 1
Consider the following model:
y = XB + E,
known as the Classical Linear Regression Model (CLRM), where y is the
dependent variable, X is the set of independent variables, ß is the vector
of parameters to be estimated and & is the error term.
a) List and discuss the assumptions you need for the Ordinary Least
Squares (OLS) to be a Best Linear Unbiased Estimator (BLUE).
b) Derive the OLS estimator, discussing where the assumptions are
needed for the derivation.
c) Discuss the properties of linearity, unbiasedness and efficiency,
discussing what assumption you need for each of these properties to
hold.
d) Present and discuss the R² and the adjusted R². Why the adjusted R²
should be regarded as a "soft rule"? Discuss.
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