Econ B2000, MA Econometrics

Kevin R Foster, the Colin Powell School at the City College of New York, CUNY

Fall 2019

For this lab, we improve some of our regression models to explain wages.

Form a group of 3 (again, invite someone new). Groups should prepare a 4-min presentation by one of the group members about their experiment process and results. You get 45 min to prepare.

Build on the previous lab in creating useful models.

Concentrate on a smaller subset than previous. For instance if you wanted to look at wages for Hispanic women with at least a college degree, you might use

attach(acs2017_ny)
use_varb <- (AGE >= 25) & (AGE <= 55) & (LABFORCE == 2) & (WKSWORK2 > 4) & (UHRSWORK >= 35) & (Hispanic == 1) & (female == 1) & ((educ_college == 1) | (educ_advdeg == 1))
dat_use <- subset(acs2017_ny,use_varb) # 
detach()

Your group should pick a different subset!

Try a regression with age and age-squared in addition to your other controls. What is the peak of predicted wage? What if you add higher order polynomials of age, such as \(Age^3\) or \(Age^4\)? Do a hypothesis test of whether all of those higher-order polynomial terms are jointly significant. Describe the pattern of predicted wage as a function of age. What if you used \(log(Age)\)? (And why would polynomials in \(log(Age)\) be useless?)

Recall about how dummy variables work. If you added educ_hs in a regression using the subset given above, what would that do? (Experiment, if you aren’t sure.) What is interpretation of coefficient on educ_college in that subset? What would happen if you put both educ_college and educ_advdeg into a regression? Are your other dummy variables in the regression working sensibly with your selection criteria?

Why don’t we use polynomial terms of dummy variables?

What is difference in regression from using log wage as the dependent variable? Compare the pattern of predicted values from the two models (remember to take exp() of the predicted value, where the dependent is log wage). Discuss.

What are the other variables you are using in your regression? Do they have the expected signs and patterns of significance? Explain if there is a plausible causal link from X variables to Y and not the reverse. Explain your results, giving details about the estimation, some predicted values, and providing any relevant graphics. Impress.