Jeffrey Wooldridge
@jmwooldridge.bsky.social
Econometrics professor and author. Dogs = 2, cats >= 10.
It was on my mind because the batteries in my laser pointer died just before a presentation. 😂
November 9, 2025 at 8:10 PM
It was on my mind because the batteries in my laser pointer died just before a presentation. 😂
Did you find any AAA batteries?
November 8, 2025 at 12:44 AM
Did you find any AAA batteries?
Does it help if you call it a “folder”?
October 13, 2025 at 7:20 PM
Does it help if you call it a “folder”?
I’m still trying to get students to stop sending me papers called “draft.docx.”
October 13, 2025 at 3:58 PM
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.
September 23, 2025 at 6:45 PM
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.
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.
September 23, 2025 at 6:45 PM
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.
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.
September 23, 2025 at 6:45 PM
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.
This puzzled me and seems like a kind of appropriation. Mundlak was squarely in the frequentist/FE camp.
September 16, 2025 at 10:24 PM
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.
September 14, 2025 at 2:19 PM
Oh I think you know I’ve always been a barbarian.
Nothing casual about it. I take these things very seriously.
July 8, 2025 at 5:58 PM
Nothing casual about it. I take these things very seriously.
Finally, I have my Flintstones name.
July 8, 2025 at 5:20 PM
Finally, I have my Flintstones name.
This inspires me to start a collection of pet rock econometricians.
July 6, 2025 at 7:03 PM
This inspires me to start a collection of pet rock econometricians.
Reposted by Jeffrey Wooldridge
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().
There's also now betwfe(), which implements a bridge-penalized (includes lasso and ridge regression) version of etwfe().
![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](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:v5yhsvcygebhudec4rhb35wx/bafkreihi2xmgx3rse6inv2nh3pe74s7d5ir4ypwnlgchqcnpo7muv7cq3y@jpeg)
July 5, 2025 at 8:39 PM
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().
There's also now betwfe(), which implements a bridge-penalized (includes lasso and ridge regression) version of etwfe().