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.

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.
1. Their going to the soccer game today.
2. They’re going to the soccer game today.
3. There going to the soccer game today.

1. The council has made it’s decision.
2. The council has made its decision.
3. The council has made their decision.

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More from @jmwooldridge

30 Apr
Tests that should be retired from empirical work:

Durbin-Watson statistic.
Jarque-Bera test for normality.
Breusch-Pagan test for heteroskedasticity.
B-P test for random effects.
Nonrobust Hausman tests.

I feel uncomfortable using names, but this is #metricstotheface.
D-W test only gives bounds. More importantly, it maintains the classical linear model assumptions.
J-B is an asymptotic test. If we can use asymptotics then normality isn't necessary.
B-P test for heteroskedasticity: maintains normality and constant conditional 4th moment.
B-P test for RE: maintains normality and homoskedasticity but, more importantly, detects any kind of positive serial correlation.
Nonrobust Hausman: maintains unnecessary assumptions under the null that conflict with using robust inference. Has no power to test those assumps.
Read 5 tweets
27 Apr
Historically, economics has fallen into the bad habit of thinking the fancier estimation method is closer to being "right" -- based on one sample of data. We used to think this of GLS vs OLS until we paid careful attention to exogeneity assumptions.

We used to think this of 2SLS vs OLS until problems with weak instruments were revealed.

We still seem to think matching methods are somehow superior to regression adjustment if they give somewhat different estimates.
And we now seem to think ML methods are to be preferred over basic methods. Perhaps a reckoning is on its way.
Read 4 tweets
24 Apr
Yesterday I was feeling a bit guilty about not teaching lasso, etc. to the first-year PhD students. I'm feeling less guilty today. How much trouble does one want to go through to control for squares and interactions for a handful of control variables?

And then it gets worse if I want my key variable to interact with controls. You can't select the variables in the interactions using lasso. I just looked at an application in an influential paper and a handful of controls, some continuous, were discretized.
Discretizing eliminates the centering problem I mentioned, but in a crude way. So I throw out information by arbitrarily using five age and income categories so I can use pdslasso? No thanks.
Read 8 tweets
24 Apr
I was reminded of the issue of centering IVs when creating an example using PDS LASSO to estimate a causal effect. The issue of centering controls before including them in a dictionary of nonlinear terms seems it can be important.

The example I did included age and income as controls. Initially I included age, age^2, inc^2, age*inc. PDSLASSO works using a kind of Frisch-Waugh partialling out, imposing sparsity on the controls.
But as we know from basic OLS, not centering before creating squares and interactions can make main effects weird -- with the "wrong" sign and insignificant. This means in LASSO they might be dropped.
Read 6 tweets
24 Apr
It seems like every week, if not more frequently, I learn something new about a basic estimation method -- OLS, 2SLS, and offshoots. My students seem skeptical when I tell them this but it's true.

This week: centering before creating squares and interactions.

Now, I've taught this in the context of OLS and 2SLS for a long time, and it comes up a lot in my introductory book. It's often needed to give main effects a sensible interpretation -- whether those are exogenous or endogenous, whether it's a pooled method or FE.
But one case where I've been too cavalier is with creating instruments out of squares and interactions of exogenous variables when, say, the structural equation includes w*xj where w is endogenous and xj is exogenous. We can use xj*zh as IVs.
Read 6 tweets
7 Apr
I've decided to share a Dropbox folder containing a recent paper -- a sort of "pre-working" paper -- on panel data estimators for DID/event studies. I'm "in between" web pages (and could use recommendations on a simple, effective platform).

The paper starts with algebraic equivalence results -- hence the somewhat odd title -- and applies those to interventions with common entry time and staggered entry. I think it's useful to see the equivalence between TWFE with lots of heterogeneity and pooled OLS equivalents.
I think of it as a parametric regression adjustment version of Callaway and Sant'Anna (but using levels rather than differences) And, as in Sun and Abraham, I make a connection with TWFE (while allowing for covariates).
Read 6 tweets

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