Introductory Econometrics
Midterm
1. QUESTION A1
The following estimated regression model provides evidence on the relationship between age (age) and log of wage (log(wage)) after controlling for number of siblings (sibs). The sample size is 935 and R2 = 0.048. Standard errors are presented in brackets ().
Regression equation here “Because of the very low R2
(0.048) there is little evidence of a statistically significant association between age and log(wage)”. This statement is
(a) True
(b) False
2. QUESTION A2
The following regression model studies the effect of time spent in various activities on CEOs’ wages. CEOs are asked how many hours they spend each week in three activities: working, leisure, and sleeping. Any activity is put into one of the three categories, so that for each CEO, the sum of hours in the three activities must be 168.
wage = β0 + β1work + β2leisure + β3sleep + u
Does this model violate any Gauss-Markov assumption?
(a) No, the model does not violate any Gauss-Markov assumption.
(b) The model violates the “no perfect collinearity” assumption.
(c) The model violates the “zero conditional mean” assumption.
(d) The model violates the “normal distribution of the error term” assumption.
3. QUESTION A3
The following estimated regression model explains fertility represented by the total num-ber of children born to a women (kids).
where t_educ refers to education of the women in years minus 13,
t_age refers to age of the women in years mius 43,
and t_agesq is t_age squared.
Interpret the constant term.
4. QUESTION A4
Suppose you take a random sample of undergraduate students drawn from those enrolled in the University of New South Wales and you find that students who regularly attend tutorials get a mark 15% higher in the final exam than those who do not attend regularly. Which of the following is NOT an appropriate conclusion to be drawn from this finding?
(a) Whether students attend tutorials or not will not be random and thus the finding will likely to be biased.
(b) The evidence suggests there is a positive correlation between attending tutorials and final-exam marks.
(c) Because the sample is random, it is likely that the result represents the causal effect of attending tutorials on final-exam marks.
(d) Whether students attend tutorial or not is likely to be highly correlated with observ-able factors such as whether they are a foreign student or not, or whether they are a hard-working student or not. If these factors were controlled for in the regression, then the result could possibly be given a causal interpretation.
5. QUESTION A5
You have data on all the high school students in Australia on their math score (math) (scores range 0 to 10) and their school per-student spending (expend) and you are inter-ested in estimating the following population regression.
math = β0 + β1expend + u
Suppose that you present your model to your friend, Federico, and he argues that it is highly unlikely that the error term u is distributed like a normal distribution and therefore you are not able to use OLS to test whether β > 0.
Is Federico right or wrong? Explain why.