QuantEcon

QuantEcon is a @NumFOCUS fiscally sponsored project dedicated to developing open source computational tools for economics. Supported by the @SloanFoundation.

[QuantEcon @QuantEcon]

Pure, beautiful abstract Dynamic Programming theory❣️❣️❣️

#DynamicProgramming #EconomicTheory #QuantEcon #EconTwitter

[John Stachurski @john_stachurski]

Tom and I have finally finished a draft of Dynamic Programming Vol 2! Exhausting but satisfying. New approach to DP theory, advanced material, many applications... https://t.co/PPDk98DFgV

[QuantEcon @QuantEcon]

We’re super delighted to host Simon’s new book, Deep Learning for Solving and Estimating Dynamic Models in Economics and Finance❣️❣️❣️

[Simon Scheidegger @comp_simon]

Thrilled that "Deep Learning for Solving and Estimating Dynamic Models in Economics and Finance," found a home at @QuantEcon. Access the e-book/code here: https://t.co/CAvYWnUW1H . Huge thanks to Matt McKay, @john_stachurski , Tom Sargent, QuantEcon team for making this happen!

[alberto bisin @albertobisin]

Tom Sargent and John Stachurski have written a @QuantEcon note on this (h/t @aledinola)

https://t.co/EisaJ1U9gS

[Simons Foundation @SimonsFdn]

During today's #ICM2026 opening ceremonies, the IMU announced the 2026 Fields Medals recipients:

@UChicago's Yu Deng, @stonybrooku's John Pardon, @UofT's Jacob Tsimerman, and Hong Wang of @nyuniversity and @Institut_IHES.

Read more: https://t.co/oJARAI8I8j

[QuantEcon @QuantEcon]

Definitely should not miss this❣️❣️❣️

[Simon Scheidegger @comp_simon]

Thrilled to share a project I've been refining: a complete, open-source repository on "Deep Learning for Solving and Estimating Dynamic Models in Economics and Finance."
I've cleaned up the materials from my PhD classes and summer schools into one coherent resource. 🧵 1/6

[QuantEcon @QuantEcon]

Exciting to see our co-founder @john_stachurski leading a fantastic workshop on Advanced Computational Method at @CarnegieMellon ❣️❣️❣️

See more here:
https://t.co/goeLSzY34l

#Python #GoogleJAX #EconTwitter

[QuantEcon @QuantEcon]

Beautiful and deeply deserved.
Truly grateful for your guidance and the opportunity to work with you❣️❣️❣️

[John Stachurski @john_stachurski]

I know we shouldn't be driven by desire for accolades but I am proud of this one. Maybe I didn't entirely waste my short moment of time on this beautiful planet 🥹❤️ https://t.co/BKW3aMxAfq @SocCompEcon

[QuantEcon @QuantEcon]

@40yoap @john_stachurski A bit like saying Newton is enough so we don’t need quantum mechanics—true in many domains, but not all 😉

[QuantEcon @QuantEcon]

Dynamic Programming II just got its final chapter - - -
Approximation + reinforcement learning — free forever ❣️❣️❣️

#DynamicProgramming #QuantEcon #OpenSource #EconTwitter

[John Stachurski @john_stachurski]

We added a new and final chapter to Dynamic Programming Vol II (DP2) on approximation and reinforcement learning: https://t.co/PPDk98DFgV. DP1 and DP2 will always remain freely available online -- give me liberty or give me death 🤟💀

[QuantEcon @QuantEcon]

QuantEcon lectures can now be launched straight into Google Colab—no local setup needed🎉🎉🎉

Thanks @jlperla for making this even more accessible❣️❣️❣️

#JuliaLang #QuantEcon #OpenSource #ComputationalEconomics #EconTeaching #JuliaNotebooks #GoogleColab #Economics #DataScience

[Jesse Perla @jlperla]

In the latest @QuantEcon Julia release, you can now launch notebooks directly in @GoogleColab.

Just click ▶️ on the online lecture to open it in Colab.
On first run, the notebook automatically installs all required packages.

[QuantEcon @QuantEcon]

A big new release for Quantitative Economics with Julia 🎉 🎉 🎉

Thanks @jlperla for the overview — lots of improvements ❣️❣️❣️

#QuantEcon #JuliaLang #ComputationalEconomics #OpenSource

[Jesse Perla @jlperla]

https://t.co/Ljp2pSbmHc

[QuantEcon @QuantEcon]

@pzoch5 @RBCtabris wow😍😍😍

[QuantEcon @QuantEcon]

Super happy to see Dynamic Programming by Sargent & Stachurski out in the world — rigorous, elegant, and full of applications. A beautiful piece of work❣️❣️❣️

[Cambridge Economics @CambUP_Econ]

Dynamic Programming by Thomas J Sargent and John Stachurski
Provides a rigorous and unified presentation of recent advances in dynamic programming, along with code, algorithms, and many applications.
📚 https://t.co/5k26qTjsj5

[QuantEcon @QuantEcon]

We keep revising our Jax lectures❣️❣️❣️

https://t.co/Dar7F5nJHW

#Google #Python

[Google Open Source @GoogleOSS]

From hours to seconds 🤯. An economist from @QuantEcon tells the story of how JAX's simple and expressive API transformed a critical economic model for the Central Bank of Chile, making high performance accessible. #JAX #SciComp #Economics #Python
https://t.co/m1SVuOFr1B

[QuantEcon @QuantEcon]

Should not miss it❣️❣️❣️

[Simon Scheidegger @comp_simon]

The tentative program for the summer school/conference on "Deep Learning in Economics and Finance (August 25-29)" is online: https://t.co/QFxxce3L5z, and https://t.co/PUcv25Mhem @YuchengYang1993 @glviolante @unito @nuvoloscloud

[QuantEcon @QuantEcon]

Great❣️It’s always nice to have more options available‼️

https://t.co/C9nmdahVi2

[Mizuhiro Suzuki @mizuhiro_suzuki]

I added a notebook to @QuantEcon Notes. It shows how to use Julia JuMP for OLS and ML estimations.
Of course the purpose of the document is not to argue that we should use JuMP for OLS or ML! I wanted to see how JuMP could accommodate estimations with data.

[QuantEcon @QuantEcon]

We have just added a new lecture on Neural Network using Keras and JAX.
❣️Don’t miss it.❣️

https://t.co/ks7E09jzEO

[Jesse Perla @jlperla]

Also, a reminder that @quantecon has rapidly expanding content for other frameworks, including Python JAX https://t.co/PKWrSQPaxr