Noemi Banerjee-Duflo!
RD News
Help is at hand from Calonico, Cattaneo, and Titiunik
Three (3) new Stata commands, no less!
rdrobust: new robust, bias-corrected confidence intervals
rdbwselect: bandwidth selection this way and that
rdbinselect: Automated binwidth selection for those figs where you’re not doing any smoothing
Published November 23, 2012 | Tagged Questions, Reader Comments, Regression Discontinuity Leave a comment
Signs of aging …
From: Martin Van der Linden
In chapter 1 page 6, you mention the case of the effect of start age on results in school as and example of fundamentally unidentified questions. Do you mean that what cannot be assessed experimentally is the very effect of starting school later because a student who starts school at 6 and is perfectly identical in all dimensions but start age to another student starting school at 7 cannot be found?
If true, is there any reason we would like to measure this pure effect of start age independently of maturation effect? Isn't it precisely maturation effect we try to measure when thinking about start age? yes! any first grader who started at 7 will be older than a first grader who starts at age 6 on test day. Since we think there are big age-at-test effects, the comparison in test scores between these two is misleading Many school districts would like to boost their test scores and are tempted to go for an older start age to do it. Older start ages will indeed boost scores (suppose you couldn't enter first grade until after your bar mitzvah ...) But that fact doesn't mean older entrants are learning more; they might well do worse (e.g., by virtue of the dropout age mechanism detailed in Angrist and Krueger 1991) JA
Probit better than LPM?
From Mark Schaffer:
Question: Dave Giles, in his econometrics blog, has spent a few blog entries attacking the linear probability model.
http://davegiles.blogspot.co.uk/2012/06/another-gripe-about-linear-probability.html
http://davegiles.blogspot.co.uk/2012/06/yet-another-reason-for-avoiding-linear.html
The first of these is the more convincing (at least for me): he cites Horace & Oaxaca (2006) who show that the LPM will usually generate biased and inconsistent estimates. Biasedness doesn’t bother me so much but inconsistency does, especially as it apparently carries over to estimates of the marginal effects.
Dave’s conclusion is that one should use probit or logit unless there are really good reasons not to (e.g., endogenous dummies or with panel data).
You’ve been staunch defenders of estimating the LPM using OLS, so I’d be very interested to see your views on this.
Best wishes,
Mark Schaffer
There are three arguments here: (1) The LPM does not estimate the structural parameters of a non-linear model (Horace and Oaxaca, 2006); (2) the LPM does not give consistent estimates of the marginal effects (Giles blog 1) and (3) the LPM does not lend itself towards dealing with measurement error in the dependent variable (Giles blog 2). The structural parameters of a binary choice model, just like the probit index coefficients, are not of particular interest to us. We care about the marginal effects. The LPM will do a pretty good job estimating those. If the CEF is linear, as it is for a saturated model, regression gives the CEF – even for LPM. If the CEF is non-linear, regression approximates the CEF. Usually it does it pretty well. Obviously, the LPM won’t give the true marginal effects from the right nonlinear model. But then, the same is true for the “wrong” nonlinear model! The fact that we have a probit, a logit, and the LPM is just a statement to the fact that we don’t know what the “right” model is. Hence, there is a lot to be said for sticking to a linear regression function as compared to a fairly arbitrary choice of a non-linear one! Nonlinearity per se is a red herring. As for measurement error, we would welcome seeing more applied work taking this seriously. Of course, plain vanilla probit is not the answer.
SP
Published July 9, 2012 | 4 Comments
Fixed effects (DD) and LDV bracketing example
On p. 246, we reference Guryan (2004) as a scenario where DD and lagged dependent variables methods bracket the causal effect of interest (see also Section 5.4) . . . except that the editors and/or referees wrote that out of Guryan’s script. This argument does appear, however, in his 2001 working paper, available here
Published June 8, 2012 | Tagged Corrections, difference-in-differences, Questions, Reader Comments Leave a comment
Fixed effects and lagged dependent variables again
chaos6174 asks:
In section 5.3 Fixed Effects versus Lagged Dependent Variables,you write that the fixed effect model can deal with the OVB problem caused time-invariant or group-invariant omitted variables,and also give some suggestions on the strategies handing the OVB problem caused by potential omitted variables that may change over time in practice.On whether to employ fixed individual effects model or lagged dependent variable model ,you suggest that the researchers Â…"find broadly similar results using both models".But if the estimated coefficient of the interested explanatory variable from the two models differ tremendously,what shall be done to for the regressions to make sense? Thanks.
The fixed effects and lagged dependent variable models are different models, so can give different results. We discuss this on p. 245-46 in the book. If the results are very different you could consider estimating a model with both fixed effects and a lagged dependent variable. As we discuss in the book, this is a challenging model to estimate. As you can see from our discussion we don’t think the approaches you need to instrument for the lagged dependent variable are all that compelling, so this is not a clean solution. You can also think about the simple FE and LDV results as bracketing the true effect. Of course, if the difference is large this may not be particularly informative. If all else fails there may not be much you can do other than find another approach/other data/a better natural experiment to study the research question you are after.
SP
Published May 31, 2012 | Tagged difference-in-differences, fixed effects, laggged dependent variable 2 Comments
Testing DD
Jessie asks: Is there a nonparametric (or parametric) test that can be
used to test whether the treatment and control group are similar in
difference-in-difference before the treatment occurs?
For example, in the Card-Krueger minimum wage example (Figure 5.2.1),
would there be a test to check whether the trend in the employment
rate was statistically similar between PA and NJ before the 1992
minimum wage change?
I can only think of doing a linear regression (or perhaps another
parametric form?) on the 2 series and testing whether the coefficient
on the time variable in the NJ regression pre-1992 is statistically
different from that of the PA regression pre-1992.
Thank you!
Good question jessie. You can think of
the "Granger Causality" approach taken in Autor (2003)
and described in chapter 5 as version of the
test you have in mind. If there appears to be
a treatment effect before treatment, that's evidence of
diverging trends.
Ultimately, what matters most is whether allowing for differential
trends changes results in a meaningful way, not whether the trends
themselves are statistically significant. Chapter 5 suggests
adding linear group-specific trends (e.g., state-specific trends)
as a spec test.
Note that this won't work in the original NJ/PA min wage study.
Why not?
JA
Perry Preschool Subjects are Ageless
Observant reader Oliver Jones noticed that the Perry subjects can not be age 27 in 1993, as we mistakenly implied on page 11. Rather, 1993 is the publication date of a study looking at the Perry subjects when they were 27.