Rachel Leah Childers (@DonskerClass) header
Rachel Leah Childers (@DonskerClass) profile picture

Econometrics, Statistics, Computational Macroeconomics, Confusion. (Mostly the last.)

Zürich, CH
Joined June 2013
Photo by Rachel Leah Childers (@DonskerClass)Photo by Rachel Leah Childers (@DonskerClass)Photo by Rachel Leah Childers (@DonskerClass)Photo by Rachel Leah Childers (@DonskerClass)Photo by Rachel Leah Childers (@DonskerClass)Photo by Rachel Leah Childers (@DonskerClass)Photo by Rachel Leah Childers (@DonskerClass)Photo by Rachel Leah Childers (@DonskerClass)Photo by Rachel Leah Childers (@DonskerClass)Photo by Rachel Leah Childers (@DonskerClass)
For Spring semester, I'm bringing back free weekly open office hours for anyone in the world with Econometrics questions. Tuesdays 4-6 PM Central European Time (US EST 10AM-12PM) or by appointment; sign up and drop by!
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With the rising tide of slop papers, I can no longer mainline arXiv, so I decided to fight slop with slop and vibecode a recommender system based on my library and own interests and papers to sort and select new ones that I would care about. Now live at: donskerclass.github.io/paper…
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The format was inspired by @DrewDim's more ambitious personalized paper sorter: papers.ddimmery.com Sorting is mostly tf-idf with similarity comparisons to small embeddings. Code at github.com/donskerclass... though the hardest part was assembling my own library data.
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Rachel Leah Childers retweeted
Last week, we hosted the First Chapel Hill - Copenhagen Conference on Macroeconomics and Deep Learning @UNC_econ. The conference was jointly organized by @JeppeDruedahl, @rabinovich_stan, Will Jungerman and myself. We were honored to have a brilliant keynote by @UncertainLars.
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Rachel Leah Childers retweeted
It's a wrap! Very proud to have hosted a fantastic workshop on new methods & models in macro. Two days, ten terrific talks, many lively debates on new models, computation, AI, rational expectation & RL, state/sequence/path space, and beyond. A thread on the talks & frontier🧵
Very much looking forward to the 𝐙𝐮𝐫𝐢𝐜𝐡 𝐐𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐌𝐚𝐜𝐫𝐨 𝐖𝐨𝐫𝐤𝐬𝐡𝐨𝐩 next week! Excited to welcome an outstanding group of macroeconomists pushing the very frontier: Tom Sargent, @ben_moll, @a_auclert, @MarlonAzinovic, and many others! @Florian_Scheuer @comp_simon
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Rachel Leah Childers retweeted
For Spring semester, I'm bringing back free weekly open office hours for anyone in the world with Econometrics questions. Tuesdays 4-6 PM Central European Time (US EST 10AM-12PM) or by appointment; sign up and drop by!
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Sign up for free open Econometrics office hours at donskerclass.github.io/Offic…
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Go see Emily to learn about principled ways to use synthetic data!
I’m at NeurIPS this week (12/2-12/8) to present our work on when/how synthetic data (e.g., LLM simulations) can help scientists make inferences with less real data, improving the efficiency of costly experiments. Come by Poster #904 on Thursday 4:30PM (Exhibit Hall C,D,E)!🙂
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Rachel Leah Childers retweeted
We are very proud to present our outstanding 2025/26 cohort of job market candidates from the @UZH_en Department of Finance/Swiss Finance Institute! ✨✨✨✨✨ df.uzh.ch/en/studies/phd/peo… Please find their research interests and job market papers below👇👇👇
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I will be at University of Tübingen on Tuesday Dec 2, and at NeuRIPS in San Diego Dec 3-8 spreading the good word of the Generalized Method of Moments in the age of AI. Econometrics lovers come say hi!
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Rachel Leah Childers retweeted
Updated Fall time for free weekly open office hours for anyone in the world with Econometrics questions. Wednesdays 3:30-5:30PM Central European Time (US EDT 10:30/EST 9:30AM) or by appointment; sign up and drop by!
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Updated Fall time for free weekly open office hours for anyone in the world with Econometrics questions. Wednesdays 3:30-5:30PM Central European Time (US EDT 10:30/EST 9:30AM) or by appointment; sign up and drop by!
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Econometrics Free Open Office Hours details and signup at: donskerclass.github.io/Offic…
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Valid inference w/ LLM-simulated data: 1. Take subsample of texts, extract vars 2. Construct moments identifying param on those vars 3. Ask LLM to simulate vars on sample & remaining texts 4. Use same moments w/ simulated vars 5. Combine moments, estimate jointly w/ 2-step GMM
💡Can we trust synthetic data for statistical inference? We show that synthetic data (e.g. LLM simulations) can significantly improve the performance of inference tasks. The key intuition lies in the interactions between the moments of synthetic data and those of real data
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Working on this project to understand how LLM simulation data could contribute to drawing valid scientific conclusions from real world data, led by @yewonbyun_ with @shantanug7, was a great learning experience. Happy to see it forthcoming at #NeurIPS2025
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For today's meeting, I read about Forward-Backward Stochastic Differential Equations. Among other things, these allow for a stochastic version of Pontryagin's maximum principle for optimal control along a path. Some notes I made explaining how they work: donskerclass.github.io/misc/…
Excited to kick off the Zurich 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠+𝐌𝐚𝐜𝐫𝐨 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 𝐆𝐫𝐨𝐮𝐩, organized with @DonskerClass & Felix Kubler! Faculty/students @UZH_en & @ETH_en will meet weekly to discuss both core CS papers and frontier economic applications.
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Rachel Leah Childers retweeted
Thrilled to share that Rachel Childers @DonskerClass has joined @UZH_en as a Senior Research Associate! Her pioneering work on heterogeneous agent models with machine learning & perturbation methods has inspired much recent work. Welcome to Zurich, Rachel! donskerclass.github.io/
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For the Spring semester, I am restarting my free weekly open office-hours for anyone in the world with Econometrics questions. Wednesdays 10-12 AM Eastern or by appointment; sign up and drop by!
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Link for Open Econometrics Office Hours details and sign-up: donskerclass.github.io/Offic…
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