Tag Archives: Reader Comments
Can I get an indulgence for bad control?
We get a lot of questions about bad control. Here’s an interesting one from Colin Vance: I'd like to estimate the effect of fuel price (which I assume is exogenous) on distance driven. As a control, I would like to include the fuel efficiency of the driver's car. Although efficiency is likely to be endogenous, [...]
ivreg2 update
If you’re going to run multiple endogenous variables (not something we’re all that crazy about) you at least oughta look at the appropriate first stage Fs. And, as explained in an earlier post, we didn’t give the right formula in MHE. Luckily, a routine for first-stage F-stats in models with multiple endogenous variables is now [...]
Multiple endogenous variables – now what?!
Diligent reader Daniela Falzon, who works at the World Bank (in France . . . or Washington, DC) writes us with the following interesting problem concerning multiple endogenous variables in 2SLS: I am estimating Y = b0+ b1*X1 +b2* X2 + b3*X1*X2 + X3 Y is a dummy variable X1 is a dummy variable and [...]
Multivariate first stage F . . . NOT
This just in from the ivreg2 team (Chris Baum, Mark Schaffer, and Steve Stillman): How should you construct a first stage F stat to measure instrument strength when you have more than one endogenous variable? Not by following the instructions we gave at the bottom of page 218. Althought the theoretical expressions that motivate the [...]
Adding lagged dependent variables to differenced models
Reader Christopher Ordowich asks: In sections 5.3-5.4, there is a great discussion of using fixed effects vs. a lagged dependent variable with panel data. I am having trouble reconciling some of this discussion with a section in a recent paper by Imbens and Wooldridge (2008) titled “Recent Developments in the Econometrics of Program Evaluation.” On [...]
OLS is between the effect on the treated and the effect on controls
We learn something new (and useful!) every day . . . Macartan Humphreys of Columbia University has shown why regression estimates of treatment effects can often be expected to fall between the average effect on the treated and the average effect on controls. His theorem goes like this: Let D denote treatment, let p(X) denote [...]
Comments on Bad Control
Derek Neal of the University of Chicago comments that our discussion of bad control in section 3.2.3 leaves the impression that more control is always better as long as the controls are pre-determined relative to the causal variable of interest. The leading counter-example is the case of within-family or twins estimates that we discuss as [...]
Regression anatomy revealed