Jeffrey Wooldridge

Still time to sign up! Register on or before Dec 8. The group of attendees is shaping up nicely, and I'm looking forward to the online interactions.

@msuecon.bsky.social
@imbernomics.bsky.social

When to use negative binomial and Poisson regression When would one use a negative binomial regression and when would one use Poisson regression with respect to the mean and variance?

And here is a much better answer (from
@noahgreifer.bsky.social al, citing @jmwooldridge.bsky.social) than my mumbling in response to the Poisson vs negbin question.

stats.stackexchange.com/questions/65...

Hi! I’m Mary and I’m on the #EconJobMarket this year.

Extreme heat doesn’t just affect students, it affects the people teaching them.

JMP 🧵:

I was in New Orleans recently at a conference and I tried to reach out to Big Freedia. Apparently, we're not as tight as I thought. 😬

The times are not currently listed on the website: 10 am to 4 pm EST on both days. After each 90 minute lecture, a 30 minute Q&A.

Hope to see you then!

The next online installment of ESTIMATE: The Reduced Form is coming on Dec 11-12. I've continued to unify and expand regression-based methods to apply to exit, non-binary treatments, DDD, discrete outcomes, and more.

All proceeds to the MSU economics PhD program.

econ.msu.edu/academics/es...

It was on my mind because the batteries in my laser pointer died just before a presentation. 😂

Did you find any AAA batteries?

🚨 We are hiring! ‼️‼️

tenured track assistant or associate prof in economics at university of Melbourne @unimelb.bsky.social

welcome applications from all fields (but especially from econometrics)

econjobmarket.org/positions?sh...

Does it help if you call it a “folder”?

I’m still trying to get students to stop sending me papers called “draft.docx.”

So five different estimators when we use MLE weights collapse to one estimator using IPT weights. IPWRA and normalized AIPW work pretty well with MLE weights but differ from each other and the other estimators. The IPT-based estimator is hard to beat especially when the mean and PS are both wrong.

ssc install teffects2

The syntax is essentially the same as teffects and allows MLE or IPT logit PS estimation. Where appropriate, we allow normalized or unnormalized weights (with MLE for IPW and AIPW). Preference for normalized. Standard errors account for all estimation uncertainty.

Weights for IPW and AIPW are automatically normalized. Holds for ATE and ATT. With MLE-based weights, the three estimators are all different, and the IPW and AIPW weights are not automatically normalized. We have an accompanying Stata command, teffects2.

Thanks Paul. It’s nice to have a paper that’s both elegant (if I may say so) and practically useful. The conclusion is that using a particular covariate balancing PS estimator — inverse probability tilting — renders IPW, AIPW, and IPWRA all numerically identical with a linear conditional mean.

This puzzled me and seems like a kind of appropriation. Mundlak was squarely in the frequentist/FE camp.

Oh I think you know I’ve always been a barbarian.

We finished days 2-3 of our #summerschool in #healtheconomics

Packed with insights from
@jmwooldridge.bsky.social on recent DiD methods
StefanieSchurer on policy applications
@erdaltekin.bsky.social on the publication process

Plus great presentations throughout💪

#econbluesky

😬

Nothing casual about it. I take these things very seriously.

Finally, I have my Flintstones name.

😂😂

This inspires me to start a collection of pet rock econometricians.

A screenshot of an R console showing code and output for an extended two-way fixed effects analysis. The code loads the `fetwfe` and `did` packages, loads the `mpdta` dataset, and transforms it into `pdata` with `attgtToFetwfeDf`, specifying outcome `lemp`, time variable `year`, unit identifier `countyreal`, treatment onset `first.treat`, and covariate `lpop`. Then it runs `etwfe(pdata, time_var="time_var", unit_var="unit_var", treatment="treatment", response="response", covs="lpop")`. Below is the “Extended Two-Way Fixed Effects Results”: * **Overall Average Treatment Effect (ATT):** – Estimate: –0.0452 – Std. Error: 0.0145 – 95 % CI: \[–0.0736, –0.0167] * **Cohort Average Treatment Effects (CATT):** | Cohort | Estimated TE | SE | 95 % CI low | 95 % CI high | | ------ | ------------ | ---------- | ----------- | ------------ | | 2004 | –0.08762696 | 0.03555885 | –0.15732102 | –0.01793290 | | 2006 | –0.02127833 | 0.02128938 | –0.06300475 | 0.02044809 | | 2007 | –0.04595453 | 0.01633327 | –0.07796715 | –0.01394190 | * **Model Details:** – Units (N): 500 – Time periods (T): 5 – Treated cohorts (R): 3 – Covariates (d): 1 – Features (p): 29

First of all, I added an implementation of extended two-way fixed effects (@jmwooldridge.bsky.social 2021), etwfe(), with inputs and outputs aligned with fetwfe().

There's also now betwfe(), which implements a bridge-penalized (includes lasso and ridge regression) version of etwfe().

👀

🚨 One spot just opened up for our Summer School in Health Economics!

Top-notch lectures by
@jmwooldridge.bsky.social and Stefanie Schurer, presentations, and a great social program are waiting for you.

First come, first served – apply now!

#EconBluesky #economics @healtheconall.bsky.social

It’s a real pleasure teaching a course with @pedrosantanna.bsky.social. Many are calling us “Fire and Ice.”

See you there! Good thinking to hang out in Athens.

CPI Data Quality Declining - Apollo Academy To calculate CPI inflation, BLS teams collect about 90,000 price quotes every month covering 200 different item categories, and there...

To calculate CPI, BLS teams collect 90k price quotes every month covering 200 different item categories.
When data not available, BLS staff typically develop estimates for approximately 10% of cells in the CPI calculation. However, in May, share of data in the CPI that is estimated increased to 30%