Econometrics 2: Bivariate Linear Regression

Question 1

Explain the structure and purpose of a bivariate linear regression model in econometrics.

Answer 1

Question 2

Discuss the significance and interpretation of the error term in regression analysis.

Answer 2

Question 3

Explain the concept of linearity in regression and how it applies to model specification.

Answer 3

Question 4

Describe the process of estimating the regression line using sample data.

Answer 4

Question 5

What is the Ordinary Least Squares (OLS) method?

Answer 5

Question 6

List and describe the assumptions required for OLS to be unbiased and efficient.

Answer 6

Why they matter:
CLRA1–CLRA4 → OLS is unbiased
CLRA1–CLRA6 → OLS is BLUE (Best Linear Unbiased Estimator)
CLRA1–CLRA7 → OLS is MVUE (Minimum Variance Unbiased Estimator)

Question 7

What are the sampling distributions of OLS estimators?

Answer 7

Question 8

What changes when regressors are random instead of fixed?

Answer 8

Question 9

Explain how R^2
measures the goodness of fit in regression analysis.

Answer 9

Important identity:
TSS=ESS+RSS
(proved using OLS properties: residuals sum to 0 and are orthogonal to fitted values)

Question 10

How do we test hypotheses about regression coefficients?

Answer 10