University of California, Los Angeles - ECON 103
BEGINNING ECON103 EXAM
PART 1: TRUE/FALSE/EXPLAIN
1)When we drop a variable from a model, the total sum of squares (TSS) increases
Economics Mar 09, 2021
University of California, Los Angeles - ECON 103
BEGINNING ECON103 EXAM
PART 1: TRUE/FALSE/EXPLAIN
1)When we drop a variable from a model, the total sum of squares (TSS) increases.
If X and Y are independent, the conditional probability density function f(X|Y )(X|Y ) is equal to the marginal probability density function fX(X)
The Adjusted R-squared (R¯2) is always greater than or equal to the R-squared (R2).
is an unbiased estimator of µ
In the expression Pr(Y = 1) = ( 0 + 1X) from a probit model, 1 cannot be negative, since probabilities have to lie between 0 and 1.
If the true model is Y = 0+ 1X1+ 2X2+" but you omit X2 and estimate Y = 0+ 1X1+", your estimate of 1 will always be biased.
The first one is that the omitted variable (in this case X2) causes Y . Since the question says that, according to model considered, X2 is one of the variables that explains Y , this condition is likely to hold in this case.
The second condition is that the omitted variable is correlated with the variable whose coe cient may be biased. As long as X2 is not correlated with X1, the estimate of 1 will still be unbiased even if we omit X2.
In the following model, Wage = ↵0+↵1Educ+↵2Female+↵3Black+↵4Female?Educ+u, to check whether the returns to education are the same for males and females you would have to test a joint hypothesis with an F test.
Consider the model Consumption = 0 + 1Wage + ". The sample regression function estimated with OLS gives you the average (or expected) value of Consumption for each value of Wage.
Suppose you run a test of the hypothesis H0 : 1 = 0 against the two-sided alternative H1 : 1 =6 0. Your t-statistic takes the value -2.001. You therefore reject the null at the 10 % significance level.
A random variable X can only take on the following values: 0 with probability b; 10 with probability 4b; 20 with probability 4b; and 100 with probability b. Therefore b must be equal to 0.1
PART 2
You estimate the following linear probability model by OLS:
where Ji is a binary variable equal to 1 if student i obtained a job o↵er within 2 months of
graduating from UCLA and equal to 0 if he didn’t.
Explain how you would test the null hypothesis that the probability of finding a job within 2 months of graduation is the same for men and women.
Explain how you would test the null hypothesis that the increase in the probability of finding a job within 2 months of graduation associated to a 10-point increase in the GPA is the same for men and women.
Explain whether the following statement is true and why: If V ar(") is not constant, OLS will give you biased estimates of the model coe cients. (5 points.)
Somebody points out to you that your estimate of 1 is biased, as there is an omitted variable that hasn’t been included in the regression. Provide an example in which this statement would be true, specifying which two conditions must hold.
You re-estimate the model adding as a regressor the variable that was causing the bias. Based on your answer to the previous point, would you expect your new estimate of 1 to be higher or lower than before?
PART 3
A researcher is studying the relationship between the time spent watching TV and people’s attitudes towards immigration.
She has interviewed a random sample of individuals, asking them how many hours of TV they watch every week and whether they think more immigrants should be allowed into the country.
The following table is a summary of her data. Cell entries (except in the final row) are column percentages:
Hours spent watching TV ( H )
Less than 5
5 or more
All
More immigrants should be admitted ( A )
Yes
No
0.42
0.58
0.19
0.81
0.34
0.66
Sample size
200
100
300
Suppose the researcher has asked you for help running her econometric analysis and has provided you with her data. Follow the steps below to analyze them:
1. Write down the linear regression model you would use to estimate the e↵ect of hours of TV watched on the willingness to allow more immigrants into the country (call this e↵ect 1).
We’d expect ˆ1 to be negative, as increasing the numbers of hours of TV watched seems to decrease the tolerance for immigrants.
You read a study showing that poorer people spend more hours watching TV. Would this cause you to worry about the presence of bias in your estimate of 1? In what direction would the bias go? (Explain all assumptions you make about the signs of any correlation).
You doubt people can remember accurately the exact time they have spent watching TV.
Would this raise concerns about your estimate of 1? Why? .
You worry that the e↵ect of hours watching TV on the willingness to admit more immigrants may not be linear. How would you change the regression in point 1. to allow for a non-linear e↵ect? How would you test whether the relationship is linear?
You realize that your dependent variable can be expressed as the following dummy variable:
:
For the probit regression above you obtained ˆ0 = 0.50 and ˆ1 = 0.01. What is the probability that a person who spends 2 hours a week watching TV will want more immigrants to be admitted? What is the same probability for a person who spends 10 hours a week watching TV? Which of the 2 probabilities is higher? (You don’t need to provide the exact values, just set up the computation)
PART 4
You are trying to determine the e↵ect of social security on individual saving decisions in order to advise the President. You have data for a representative sample of the population that includes information on the level of social security benefits, individual income, and annual savings.
You believe that the total savings of an individual (S) are determined as a function of social security benefits (SSB), their income (I), and by the state’s location in the country ( there are 4 possible locations: Northeast (N), South (S), Midwest (M) and West (W)). Write down a regression model for the determination of total savings according to your beliefs.
Describe how you would test the following hypotheses. For each one of the hypotheses below, write down the corresponding statistic and any other regression you may need to run beyond the one used in
Part 5
The following dataset contains information on students in a class. The data is as follows:
Obs: 680
attend classes attended out of 32
termgpa GPA for term
priGPA cumulative GPA prior to term
frosh =1 if freshman soph =1 if sophomore junsen =1 if junior or senior
Note that all students are either a freshman, sophomore, junior, or senior, but no student can be more than one.
In the first regression on this page, what is the interpretation of the coefficient on attend?
Describe why you think the coefficient on attend decreased when we included priGPA in the regression.
Omitted variable bias.
Hence the coefficient on attend in the first regression was biased due to the omission of the cumulative GPA (it was biased upwards, i.e. was too high).
Once we separately controlled for the effect of the cumulative GPA, the (now unbiased) coefficient on attend became lower.
THE FOLLOWING QUESTIONS REFER TO THE REGRESSION ON THIS PAGE
10.Set up the F-statistic for the null hypothesis that the interaction terms are all equal to zero. What is the critical value you would compare it to (use a 5% level of significance, and check the critical values provided on the first
page of the exam)?
11.What is the interpretation of the interaction term fr_attend?
In the regression above, what is the standard error on the coefficient on attend?
What is the Adjusted R-squared?
What is the Total Sum of Squares?
What is the coefficient on frosh?
Part 6
Consider again the following regression (for the same variables as defined in the previous exercise):
You are concerned that the coefficient of attend may be biased due to an omitted variable. You decide to run an Instrumental Variables (IV) regression. Your instrument for “attend” is the variable “distance”, which measures how far the students live from College.
The following is part of the output from your first-stage regression:
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