Exam Questions

Question 1

Answer 1

Question 2

Answer 2

Question 3

Answer 3

Question 4

Answer 4

Question 5

Answer 5

Question 6

Answer 6

Question 7

Answer 7

Question 8

Answer 8

Question 9

Answer 9

Question 10

Answer 10

Question 11

How to explain estimated coefficients from the Multinomial probit model?

Answer 11

You can explain them using odds ratio’s.

Question 12

Answer 12

Question 13

Answer 13

Question 14

Answer 14

Question 15

How would you answer such a question?

Answer 15

Question 16

In case of truncated data (where the truncation is based on the value of the dependent variable yi), is the OLS estimator always useless?

Answer 16

Although the OLS estimator is typically inconsistent in case of truncated data, the OLS estimator can be useful as initial value for the numerical optimization of the loglikelihood function (that is, for computing the ML estimator).

Question 17

Answer 17

Question 18

(Note: in this case do not graphically illustrate)

Answer 18

Question 19

Answer 19

Question 20

When estimating the WLS estimator, when you know the variance, what should you know regarding constants?

Answer 20

Those could just be left out of the h_i