Consider the following multiple regression model: y = β0 + β1x1 + ... + βkxk + u. Which of the following statements is correct?

a.

The CLM assumptions can be summarized as, conditional on (x1, ... xk), y has a normal distribution with mean linear in x1, ..., xk and a constant variance.

b.

Whether normality of error u, and thus normality of y conditional on (x1, ... xk) can be assumed, is an empirical matter. For example, past empirical evidence suggests that normality is not a good assumption for wages.

c.

Normality of error u translates into normal sampling distributions of the OLS estimators. Any linear combination of the OLS estimators is also normally distributed, and any subset of them has a joint normal distribution.

d.

All of the above.

Question

Enrique Abbott

Mathematics

21 December, 10:34

Consider the following multiple regression model: y = β0 + β1x1 + ... + βkxk + u. Which of the following statements is correct?

a.

The CLM assumptions can be summarized as, conditional on (x1, ... xk), y has a normal distribution with mean linear in x1, ..., xk and a constant variance.

b.

Whether normality of error u, and thus normality of y conditional on (x1, ... xk) can be assumed, is an empirical matter. For example, past empirical evidence suggests that normality is not a good assumption for wages.

c.

Normality of error u translates into normal sampling distributions of the OLS estimators. Any linear combination of the OLS estimators is also normally distributed, and any subset of them has a joint normal distribution.

d.

All of the above.

+4

Answers (1)

Know the Answer?

Troy Leonard · Answer 1

Answer: Option D : All of the above.

Step-by-step explanation:

(1) The Cumulative Link Models assumptions can be summarized as, conditional on (x1, ... xk)

(2) Whether normality of error u, and thus normality of y conditional on (x1, ... xk) can be assumed, is an empirical matter

and (3) Normality of error u translates into normal sampling distributions of the OLS estimators are all correct statements describing linear regression.