CIP Session 9: Regression Analysis

Question 1

correlation and causation

Answer 1

• relation/association, nut no cause-effect
• causation: one causes the other
  Strong correlation might suggest underlying causation

Question 2

Regression Analysis

Answer 2

A statistical method of identifying the relationship between one or multiple independent variables (x) and a dependent variable (y)

Question 3

Types of data

Answer 3

• Experimental (laboratory) evidence:
• Cross-sectional: data for many subjects at the same time
• Time series data: Data collected for the same units (N) on one or multiple variables over time
• Panel/Longitundinal data: collected for the same unit over a given time period

Question 4

Ordinary Least Square (OLS) estimation and the 5 assumptions for unbiasdness, consistency and efficiency.

Answer 4

To select which fitted line fits the data best.
1. the parameters have a linear relationship
2. random sampling
3. there is variation in x
4. the mean of the error should be 0
5. Homoscedasticity; error does not depend on x

Question 5

Unbiased, consistent and efficient estimator

Answer 5

• Unbiasedness = when you calculate β for various samples, the average for each sample should be roughly the population average
• Consistency = if you increase the sample size, your β should get closer to the population average Improve the estimator by increasing the sample size.
• Efficiency = how many data points you need to have a reliable coefficient estimator for β. The more you need, the less efficient.

Question 6

What can you use to evaluate the fit of a model to data?

Answer 6

R2 tells how well the model fits the data, between 0 and 1, 1 is perfect.

Question 7

What is Omitted Variable Bias and what can you do to solve it?

Answer 7

When important variable that influence Y are omitted from the model. To solve use multiple linear regressions and add a sixth assumption: the independent variables must not be milticollinear.