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22 Jun, 5 tweets, 1 min read
Here's a panel DID question. Common intervention at t=T0. Multiple pre-treatment and post-treatment periods. Dummy d(i) is one if a unit is eventually treated. p(t) is one for t >= T0. Treatment indicator is w(i,t) = d(i)*p(t). Time constant controls are x(i).

#metricstotheface
Consider several estimators of the avg TE [coef on w(i,t)]. Period dummies are f2(t), ... fT(t).

1. Pooled OLS: y(i,t) on w(i,t), 1, d(i), p(t)
2. TWFE including w(i,t).
3. POLS: y(i,t) on w(i,t) 1, d(i), p(t), x(i)
4. POLS: y(i,t) on w(i,t) 1, d(i), f2(t), ... fT(t), x(i)
For a balanced panel without degeneracies, which is the correct statement?
I think the voting has slowed to a trickle. The correct answer is (b): All are the same. For those who didn't get this, you punishment is to read the revised version of my two-way Mundlak paper when I post it here:
dropbox.com/sh/zj91darudf2…

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More from @jmwooldridge

Jeffrey Wooldridge Profile picture
22 Jun
For my German friends: What is the German equivalent of "Ms." when addressing a woman (not yet a Dr.)? I noticed on a course application form in English -- I assume translated from German -- only two choices, "Mr." and "Mrs." Is "Frau" used for both Mrs. and Ms.?
As a follow-up: If I use English, I assume "Ms." is acceptable. I never address anyone as "Mrs." in English. It's interesting that "Frau" was translated as "Mrs." rather than "Ms." I would've expected the latter, especially in an academic setting.
My formal German courses were in the 1970s, and I learned that "Frau" is for married women only. I think I can make the adjustment, though. 🤓

I'm still intrigued that there is no "Ms." equivalent in German ....
Read 4 tweets
Jeffrey Wooldridge Profile picture
10 Jun
I should admit that my tweets and poll about missing data were partly self serving, as I'm interested about what people do. But it was a mistake to leave the poll initially vague. I haven't said much useful on Twitter in some time, so I'll try here.

#metricstotheface
I want to start with the very simple case where there is one x and I'm interested in E(y|x); assume it's linear (for now). Data are missing on x but not on y. Here are some observations.
1. If the data are missing as a function of x -- formally, E(y|x,m) = E(y|x) -- the CC estimator is consistent (even conditionally unbiased).
2. Imputing on the basis of y is not and can be badly biased.
3. Inverse probability weighting using 1/P(m=0|y) also is inconsistent.
Read 7 tweets
Jeffrey Wooldridge Profile picture
25 May
Several comments on this paper. First, it's nice to see someone taking the units of measurement issue seriously. But I still see many issues, especially when y >= 0 and we have better alternatives.

1. A search is required over units of measurement.

#metricstotheface
How do a compute a legitimate standard error of, say, an elasticity? I've estimated theta but then I ignore the fact that I estimated it? That's not allowed.

2. As with many transformation models, the premise is there exists a transformation g(.) such that g(y) = xb + u.
u is assumed to be indep of x, at a minimum. Often the distrib is restricted. In 1989 in an IER paper I argued this was a real problem with Box-Cox approaches b/c u >= -xb. If I model E(y|x) directly I need none of that. It's what Poisson regression does.
Read 6 tweets
Jeffrey Wooldridge Profile picture
9 May
A year ago on Facebook, at the request of a former MSU student, I made this post. I used to say in class that econometrics is not so hard if you just master about 10 tools and apply them again and again. I decided I should put up or shut up.

#metricstotheface
I cheated by combining tools that are connected, so there are actually more than 10 ....
1. Law of Iterated Expectations, Law of Total Variance
2. Linearity of Expectations, Variance of a Sum
3. Jensen's Inequality, Chebyshev’s Inequality
4. Linear Projection and Its Properties
5. Weak Law of Large Numbers, Central Limit Theorem
6. Slutksy's Theorem, Continuous Convergence Theorem, Asymptotic Equivalence Lemma
7. Big Op, Little op, and the algebra of them.
Read 5 tweets
Jeffrey Wooldridge Profile picture
6 May
Have we yet figured out when we should include a lagged dependent variable in either time series or panel data models when the goal is to infer causality? (For forecasting the issue is clear.) Recent work on macroeconomics on causal effects is a positive sign.

#metricstotheface
And the answer cannot be, "Include y(t-1) if it is statistically significant." Being clear about potential outcomes and the nature of the causal effects we hope to estimate are crucial. I need to catch up on this literature and I need to think more.
In panel data settings, if our main goal is to distinguish state dependence from heterogeneity, clearly y(t-1) gets included. But what if our interest is in a policy variable? Should we hold fixed y(t-1) and the heterogeneity when measuring the policy effect?
Read 6 tweets
Jeffrey Wooldridge Profile picture
2 May
When I teach the senior seminar to economics students I sometimes take 20 minutes to discuss common grammar mistakes. I was worried it would come off as patronizing (even obnoxious), but I actually got positive comments about it on my teaching evaluations.

#grammartotheface
I set it up as a quiz. Samples:

1. James will present his report to Kayla and I.
2. James will present his report to Kayla and me.
3. James will present his report to Kayla and myself.

1. He should not have went home early today.
2. He should not have gone home early today.
1. I should of taken less cookies.
2. I should’ve taken fewer cookies.
3. I should’ve taken less cookies.

1. She and I are going to visit my parents.
2. Her and I are going to visit my parents.
3. Her and me are going to visit my parents.
Read 4 tweets

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