ECS3706
Assignment 2 Semester 2 2024
Detailed Solutions, References & Explanations
Unique number: 399706
Due Date: September 2024
QUESTION 1
Why econometricians must know and understand the classical linear regression
assumptions:
Econometricians must understand the classical linear regression assumptions because they form
the foundation of Ordinary Least Squares (OLS) estimation, ensuring the accuracy, validity, and
reliability of the regression results. These assumptions help in:
• Ensuring unbiased estimators: When assumptions like zero correlation between error terms
and explanatory variables hold, the OLS estimators are unbiased, meaning they provide the
correct values of the population parameters on average.
Terms of use
By making use of this document you agree to:
• Use this document as a guide for learning, comparison and reference purpose,
• Not to duplicate, reproduce and/or misrepresent the contents of this document as your own work,
• Fully accept the consequences should you plagiarise or misuse this document.
Disclaimer
Extreme care has been used to create this document, however the contents are provided “as is” without
any representations or warranties, express or implied. The author assumes no liability as a result of
reliance and use of the contents of this document. This document is to be used for comparison, research
and reference purposes ONLY. No part of this document may be reproduced, resold or transmitted in any
form or by any means.
Assignment 2 Semester 2 2024
Detailed Solutions, References & Explanations
Unique number: 399706
Due Date: September 2024
QUESTION 1
Why econometricians must know and understand the classical linear regression
assumptions:
Econometricians must understand the classical linear regression assumptions because they form
the foundation of Ordinary Least Squares (OLS) estimation, ensuring the accuracy, validity, and
reliability of the regression results. These assumptions help in:
• Ensuring unbiased estimators: When assumptions like zero correlation between error terms
and explanatory variables hold, the OLS estimators are unbiased, meaning they provide the
correct values of the population parameters on average.
Terms of use
By making use of this document you agree to:
• Use this document as a guide for learning, comparison and reference purpose,
• Not to duplicate, reproduce and/or misrepresent the contents of this document as your own work,
• Fully accept the consequences should you plagiarise or misuse this document.
Disclaimer
Extreme care has been used to create this document, however the contents are provided “as is” without
any representations or warranties, express or implied. The author assumes no liability as a result of
reliance and use of the contents of this document. This document is to be used for comparison, research
and reference purposes ONLY. No part of this document may be reproduced, resold or transmitted in any
form or by any means.
