The purpose of this in-class lab is to better understand omitted variable bias and multicollinearity. The lab should be completed in your group. To get credit, upload your .R script to the appropriate place on Canvas.

## For starters

Open up a new R script (named ICL5_XYZ.R, where XYZ are your initials) and add the usual “preamble” to the top:

library(tidyverse)
library(broom)
library(wooldridge)
library(modelsummary)

Also install the package car by typing in the console:

install.packages("car", repos='http://cran.us.r-project.org')

library(car)

The car package allows us to easily compute useful statistics for diagnosing multicollinearity.

We’ll use a new data set on wages, called wage2.

df <- as_tibble(wage2)

Check out what’s in the data by typing

glimpse(df)
# or, equivalently
datasummary_df(df)

We can also look at summary statistics in the data by typing

datasummary_skim(df,histogram=FALSE)

## Properties of Omitted Variables

Think of the following regression model: $\log(wage) = \beta_0 + \beta_1 educ + \beta_2 IQ + u$ where $$wage$$ is a person’s hourly wage rate (in cents, not dollars).

We want to verify the property in Wooldridge (2015) that $$\widetilde{\beta}_1 = \hat{\beta}_1+\hat{\beta}_2 \widetilde{\delta}_1$$, where $$\widetilde{\beta}_1$$ comes from a regression of $$log(wage)$$ on $$educ$$, $$\widetilde{\delta}_1$$ comes from a regression of $$IQ$$ on $$educ$$, and the $$\hat{\beta}$$’s come from the full regression (in the equation above).

First, run a regression of IQ on educ to obtain $$\widetilde{\delta}_1$$:

est1 <- lm(IQ ~ educ, data=df)
tidy(est1)

Now run a regression of log wage on educ to obtain $$\widetilde{\beta}_1$$. Note: You’ll need to create the log wage variable first. If you can’t remember how to do that, refer back to previous labs.

est2 <- lm(logwage ~ educ, data=df)
tidy(est2)

Now run the full regression of log wage on educ and IQ to obtain $$\hat{\beta}_1$$ and $$\hat{\beta}_2$$. Verify that $$\widetilde{\beta}_1 = \hat{\beta}_1+\hat{\beta}_2 \widetilde{\delta}_1$$.

est3 <- lm(logwage ~ educ + IQ, data=df)
tidy(est3)
est2$coefficients["educ"] == est3$coefficients["educ"] +
est3$coefficients["IQ"]*est1$coefficients["educ"]

(The last line returns TRUE if the equality holds and FALSE if it doesn’t hold.)

We can also look at the output with modelsummary():

modelsummary(
list(est1,est2,est3)
)

Is $$\widetilde{\beta}_1$$ larger or smaller than $$\hat{\beta}_1$$? What does this mean in terms of omitted variable bias?

## Multicollinearity

Now let’s see how to compute diagnostics of multicollinearity. Recall from Wooldridge (2015) that multicollinearity can better be thought of as “a problem with small sample sizes.” Let’s use the meapsingle data set from the wooldridge package. We are interested in the variable pctsgle which gives the percentage of single-parent families residing in the same ZIP code as the school. The outcome variable is math4, which is the percentage of students at the school who passed the 4th grade state test in math.

Load the data and run a regression of math4 on pctsgle. (I won’t include the code, since this should be old hat by now.) Interpret the slope coefficient of this regression. Does the effect seem large?

Now consider the same model, but with lmedinc and free as additional regressors. lmedinc is the log median household income of the ZIP code, and free is the percent of students who qualify for free or reduced-price lunch. Do you think there might be a strong correlation between lmedinc and free? Compute the correlation. Does it have the sign you would expect? Do you think it’s close enough to 1 that it would violate the “no perfect collinearity” assumption?

cor(df$lmedinc,df$free)

Now run the model with pctsgle, lmedinc, and free as regressors. (Again, I won’t include the code here.)

Comment on the value of the pctsgle coefficient, compared to the first regression you ran. What can you say about lmedinc and free as confounding variables?

### Computing variance inflation factors (VIF)

A commonly used diagnostic of multicollinearity is the VIF. We can use the vif() function from the car package to do this. Let’s compute the VIF from our estimates in the previous equation:

vif(est)

VIFs of 10 or more are typically thought to be problematic, because $$VIF = \frac{1}{1-R_j^2}$$, meaning $$R_j^2>0.9$$. See p. 86 of Wooldridge (2015).

### Is multicollinearity a problem?

Multicollinearity is typically only a problem in data sets of small sample size. As sample size increases, $$R_j^2$$ might decrease. Also, the total variation in $$x_j$$ ($$SST_j$$) increases with sample size. So multicollinearity is typically not a problem we worry about much.

# References

Wooldridge, Jeffrey M. 2015. Introductory Econometrics: A Modern Approach. 6th ed. Cengage Learning.