, ECS3706 Assignment 2 (COMPLETE ANSWERS)
Semester 2 2024 (399706) - DUE September
2024 ; 100% TRUSTED Complete, trusted solutions
and explanations.
1. Explain why econometricians must know and understand the
classical linear regression assumptions. (4)
2. Understanding the Importance of Classical Linear
Regression Assumptions for Econometricians
3. Econometricians utilize statistical methods to analyze
economic data and test hypotheses. A fundamental tool
in this analysis is the classical linear regression model,
which relies on several key assumptions. Understanding
these assumptions is crucial for several reasons:
4. 1. Validity of Inference: The classical linear regression
model is built on certain assumptions, including linearity,
independence, homoscedasticity, and normality of
errors. If these assumptions hold true, the estimators
derived from the regression will be unbiased and
consistent. This means that econometricians can make
valid inferences about relationships between variables.
For instance, if the assumption of homoscedasticity
(constant variance of errors) is violated, it can lead to
inefficient estimates and incorrect standard errors,
ultimately affecting hypothesis tests and confidence
intervals.
5. 2. Model Specification: Understanding these
assumptions helps econometricians in correctly
specifying their models. Mis-specification can occur
when relevant variables are omitted or irrelevant ones
Semester 2 2024 (399706) - DUE September
2024 ; 100% TRUSTED Complete, trusted solutions
and explanations.
1. Explain why econometricians must know and understand the
classical linear regression assumptions. (4)
2. Understanding the Importance of Classical Linear
Regression Assumptions for Econometricians
3. Econometricians utilize statistical methods to analyze
economic data and test hypotheses. A fundamental tool
in this analysis is the classical linear regression model,
which relies on several key assumptions. Understanding
these assumptions is crucial for several reasons:
4. 1. Validity of Inference: The classical linear regression
model is built on certain assumptions, including linearity,
independence, homoscedasticity, and normality of
errors. If these assumptions hold true, the estimators
derived from the regression will be unbiased and
consistent. This means that econometricians can make
valid inferences about relationships between variables.
For instance, if the assumption of homoscedasticity
(constant variance of errors) is violated, it can lead to
inefficient estimates and incorrect standard errors,
ultimately affecting hypothesis tests and confidence
intervals.
5. 2. Model Specification: Understanding these
assumptions helps econometricians in correctly
specifying their models. Mis-specification can occur
when relevant variables are omitted or irrelevant ones
