Interview Question: Dummies in an Equation

Sample Question #100 (econometrics)

What’s wrong with the following model which tries to study the "happiness measure" (the y variable) of the American population?

y = α + β1I{income<$35,000} + β2I{income>=$35,000} + β3x + ε

where I{ } is the indicator function, i.e., it’s 1 if the condition in the braces {} is true and 0 otherwise. x is a continuous exogenous variable that’s independent of income.

(Updated comment: A few visitors to my blog have complained that since not everyone knows econometrics, it would be "very" unfair to ask a question that many people simply are not familiar with. Okay, the little "contest" I had in mind was meant to be fun [like it was duly noted in the previous comments], but I see these guys have a point. So, sorry, no more contest. I apologize for the snafu. Embarrassed I hope you enjoy this question if you know statistics and/or econometrics. Cheers! 7 pm EDT 8/21/07)

Advertisements
This entry was posted in Sample Qs. Bookmark the permalink.

3 Responses to Interview Question: Dummies in an Equation

  1. Zhe says:

    may i ask what x stand for? thx

  2. Brett says:

    x is just some arbitrary independent variable. 

  3. Brett says:

    ANSWER
     
    [Again, the "contest" was cancelled]
     
    First of all, you should recognize that there’s really nothing wrong with the equation itself. It simply links y with three independent variables, plus an intercept. As a mathematical reduced form of the "happiness" model, it’s valid.
     
    The problems lie in how you estimate the model with data. There are two problems.
     
    1) Perfect collinearity. This is easy to see. The solution is to drop either α or one of the two dummies.
     
    2) Less obvious is the censored data problem. Many people in the U.S. have no reported income (such as babies and homeless people and tax evaders), so this model cannot be estimated for these people. Depending on the context of the research and your perspective, this can be a very serious issue — in fact, many financial regression models suffer this problem without the quants’ realizing it.  (Trust me, when a sharp-minded client points this out to you and your boss, you’ll be feeling mighty humiliated.)

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s