Seminars 1, 2 ,3 ,4 ,5, 6 ,7

Question 1

What questions should we put ourselves first when we first observe a regression?

Answer 1

• About the variables: is there an economic theory on them?
• About missing variables: is there any bias from omitted variables?
• About linear specifications: is it advisable to have a linearizable form?
• About indivitual significance of coefficient: what are the interpretation?
• About multicollinearity: how can we avoid it?

Question 2

What is the purpose of an economic theory in analysing variables?

Answer 2

An economic theory helps us predict the ways in which variables depend on each other.

Ex: higher education results in higher pay

Question 3

What is ommited variable bias?

Answer 3

It occurs when a key independent variable is not included in the regression model, leading to an overestimation/understimation of that model.

Question 4

What are some cases in which using the logaritmic function is a good ideea?

Answer 4

• When we want to interpret values in percentages
• When we want to solve for the asymmetry of the dependent variable
• Residuals have a “strongly” positively skewed distribution
• In the case in which we want to solve for heteroskedasticity

Question 5

What is perfect multicolinearity?

Answer 5

It occurs when two identical variables are expressed in different measurement units

Example: dummy trap

Question 6

How can you check for correlation between two variables in Eviews?

Answer 6

• Open the variables as a group
• Click View -> Covariance analysis
• Check only correlation box

Question 7

What is near multicollinearity?

Answer 7

It occurs when there is a high correlation between the independent variables.

(>0.7 or 0.8)

Question 8

How should you build an optimal regression model?

Answer 8

Include independent variables that are highly correlated with the dependent but not among themselves.

If you discover a pair of highly correlated independent variables, remove the one with the lowest correlation with the dependent one.

Question 9

What are the thresholds for the p-value?

Answer 9

• p<1% (***)
• p>1%&<5% (**)
• p>5%&<10% (*)

Question 9

What does the R squared coefficent show?

And the R squared adjusted coefficient

Answer 9

The R-squared coefficient shows the degree to which the variance of the dependent variable is explained by the independent variables.

R-squared increases with the number of variables included in the model.

ex: For R-squared=0.5 : 50% of the variance in crime rate is explained by the predictors included in the model, while the rest of 50% is explained by other predictors not included in the model

Question 10

What is the Adjusted R-squared coefficient?

Answer 10

Adjusted R-squared adds a penalty to account for the problem of R-squared.

Meaning that its value goes down as more variables are added.