Due 8am EST Wednesday Nov 8, 2017
Econ B2000, MA Econometrics
Kevin R Foster, CCNY
Each student should submit a separate assignment, even if it is an identical computer file to the rest of your study group. When submitting assignments, please include your name and the assignment number as part of the filename. Please write the names of your study group members at the beginning of your homework.
What are the names of the people in your study group?
Each person in the group should find 2 academic articles related to your current choice of final project. Write a short paragraph on each, concentrating on what data is used (and whether it is accessible), what econometric techniques, and what questions are addressed.
I used a particular subsample of the BRFSS data (no need for you to do it yourself, not yet) to estimate a logit model where the dependent variable is whether the person’s BMI would classify them as “overweight” or “obese”. I include a quadratic in age with gender interaction. These results are:
| Constant |
-2.355 |
0.430 |
| Age |
0.197 |
0.008 |
| Age^2 |
-0.0020 |
0.0001 |
| Female |
1.360 |
0.206 |
| Age*Female |
-0.100 |
0.010 |
| Age^2*Female |
0.0011 |
0.0001 |
- Are these coefficient estimates each statistically significant? Calculate t-statistics and p-values for each (there are again 105409 df).
- What is the predicted probability of being overweight for a 35-year-old male? For a female of the same age?
- At what age does male probability of being overweight peak? Female? At what levels for each?
- Next, download the BRFSS data and do some of your own estimations. BMI is a person’s weight in kg divided by their squared height in m, so a number over 25 is interpreted as overweight. The data includes a continuous variable (BMI_measure), a 0/1 dummy for overweight (d_overweight; includes overweight and obese), and a 4-category classification (X_BMI5CAT). Compare results.
- Start with some basic statistics: how does the tendency to overweight vary among educational groups? Are these differences statistically significant? Compare results from each of the 3 classifications above. Discuss.
- Next estimate a linear model of the continuous measure. Explain what variables are important to include? Discuss the results.
- Next estimate a logit and probit model of the dummy 0/1 measure. Explain what variables ought to be included or excluded. Discuss the results of the model.
- Check if you use the 4-category variable and only look at a logit or probit model of whether person is obese (BMI over 30) – how do the results change? Discuss each model and to what extent the different specifications give variant results.