Tag Archives: Reader Comments

Regression Is Matching (one more way; with discussion!)

Pat Kline has a nifty new interpretation of the old Blinder-Oaxaca regression estimator for two-group comparisons (in this case, applied to treatment effects in a selection-on-observables setup) . . .  It’s a matching estimator, of course! Here’s the version we saw at ASSA in Denver, niftier than ever! and my discussion, which is pretty good […]
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Kindle KAOS

A few disappointed readers have commented that the kindle version of MHE suuu . . . is not so hot.  Math font KAOS, Mr. Smart! (with type set by Shtarker, among other unforgivable glitches.) But ebook aficianados don’t dispair, just install one of Amazon’s free reading apps and read glitch-free on the hardware of your […]
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Corrections Coming!

Princeton University Press has graciously released a corrected version of MHE.  This is not a new edition (we’re still recovering from the first!).  But we’ve corrected the mistakes uncovered by careful readers in the past 18 months.  The corrected version is now in print and should be shipping soon from Amazon and other big retailers. […]
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Possibly Harmful Econometrics?

Northwestern finance Prof Bernard Black describes some interesting causality bloopers, a valuable caution for students and teachers alike!
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Regression anatomy revealed

Valerio Filoso from the University of Naples has written a neat Stata routine that automates the regression anatomy formula and makes a complete family of partial regression plots.  Check it out!
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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, […]
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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 […]
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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 […]
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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 […]
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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 […]
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