I'm excited to welcome Abhijit Banerjee, Bernardo Silveira and Esther Duflo as our new colleagues @econ_uzh!
As of today, Esther and Abhijit join us as Lemann Professors of Economics, and Bernardo as Professor of Applied Microeconomics.
A true quantum leap for @econ_uzh!
1/3
Recently accepted by #QJE: “Beliefs About the Economy Are Excessively Sensitive to Household-Level Shocks: Evidence from Linked Survey and Administrative Data,” by Taubinsky, Butera (@lu_butera), Saccarola, and Lian (@ChenLian92): https://t.co/bp8o1y4F1k
Super interesting!
"Inequality, Business Cycles, and Growth: A Unified Theory of Fiscal Stabilization Policies" by Alexandre Gaillard and Philipp Wangner.
"Do tax-financed fiscal expansions entail a trade-off between short-run output stabilization and long-run growth? To answer this question, we develop a heterogeneous agent framework with nominal rigidities and endogenous growth. Analytically, we characterize three regimes with potentially opposing effects on output and technology growth, depending on systematic monetary policy and tax incidence. In the U.S., the quantitatively relevant regime features a robust trade-off: a typical fiscal expansion, financed by higher tax progressivity, yields an impact output multiplier above one, a near-zero multiplier after three years, and sizable long-run output losses. Taxing the middle class avoids this trade-off and maximizes welfare."
https://t.co/gvz5edjdLu
As always, a must-read!
BIS Annual Economic Report
"Looking forward, four pressure points demand attention. First, inflation has risen. The energy supply shock has been substantial, and its effects may propagate through supply chains. ...Second, the optimism surrounding AI may not last, despite its promise of future productivity gains. The current surge in capital expenditure could prove unsustainable if supply bottlenecks restrain production. ...Third, financial vulnerabilities persist. Easy financial conditions could tighten and become a potent amplifier in adverse scenarios where interest rates rise and AI payoffs disappoint. ...Fourth, fiscal pressures are mounting. With already high debt levels, governments face rising demands for spending amid energy shocks and geopolitical tensions."
https://t.co/rGpW0eM2WJ
Hace 18 años estudiaba un Máster en Ingeniería Financiera y a la vez trabajaba como auditor de las Tesorerías y Gestoras de Fondos en el Santander.
Al acabar una clase, le pregunté a un profesor cómo aplicar en la práctica algo que acabábamos de ver en teoría.
Me dijo que, para responderme, tendría que pagarle por hora de consultoría.
Años después estudié con Damodaran, probablemente el mejor profesor de finanzas del mundo. En la primera clase nos dijo que su libro era obligatorio para la asignatura y estaba en la tienda de la Universidad. Pero que si buscábamos un poco en su web, lo encontrábamos gratis.
Los mediocres esconden, los mejores dan.
Very valuable!
"The macroeconomic effects and monetary policy implications of climate mitigation policies: results from a new quantitative analysis" (Network of Central Banks and Supervisors for Greening the Financial System)
"This report explores the monetary policy implications of climate mitigation policies using the IMF's Global Macroeconomic Model for the Energy Transition (GMMET). It shows that climate mitigation policies can generate trade-offs for monetary policymakers between stabilising inflation and economic output. The scale of these trade-offs varies across jurisdictions and depends on the specific transition policies adopted. Trade-offs are smallest when transition policies are implemented in a gradual and orderly manner but can be more substantial when policies are uncertain. Despite these near-term trade-offs, the most severe impacts of climate change occur in the absence of transition."
https://t.co/D1BiFQUIiA
MIT has a free 900+ page textbook that teaches the math behind computer science.
It is called Mathematics for Computer Science.
And it costs $0.
Most people think “learn CS” means:
Learn Python.
Learn JavaScript.
Learn React.
Learn system design.
Learn AI tools.
But under almost every serious CS topic, there is math.
Algorithms are math.
Cryptography is math.
Machine learning is math.
Databases use logic and relations.
Distributed systems use proofs and invariants.
Complexity theory is basically math with consequences.
This textbook is MIT’s foundation for that.
It covers:
Proofs
Induction
Sets
Functions
Relations
Graphs
Trees
State machines
Number theory
Modular arithmetic
Counting
Probability
Random variables
Recurrences
Asymptotic growth
Basically, the stuff that makes you understand why code works, not just how to write it.
This is the difference between someone who can follow tutorials and someone who can actually reason like an engineer.
The best part:
You do not need to be enrolled at MIT.
You do not need to pay tuition.
You do not need a professor.
You do not need a fancy bootcamp.
You can download the PDF and start reading today.
Link: https://t.co/n6iYa2nTxZ
10 free textbooks from MIT, Stanford, and Berkeley that you can download legally right now.
→ Introduction to Linear Algebra - Gilbert Strang, MIT
The textbook behind the most-watched math course in history. 20 million views on OCW. Every ML engineer learned this math from one quiet professor.
https://t.co/Q5ZHXrBuD1
→ Mathematics for Computer Science - MIT 6.042
Proofs, discrete math, probability. The actual foundation of CS that nobody tells undergrads about until it's too late.
https://t.co/FOLUDXTubX
→ Convex Optimization - Stephen Boyd, Stanford
Used in every serious ML and control systems course on earth. Cambridge University Press gave Boyd permission to keep it free on his own site.
web. stanford. edu/~boyd/cvxbook/bv_cvxbook.pdf
→ CS229 Machine Learning Notes - Andrew Ng, Stanford
Not the Coursera version. The actual Stanford graduate course notes. Dense, precise, and the closest thing to a grad school education you can download in one PDF.
https://t.co/De8lcX59zt
→ An Introduction to Statistical Learning - Stanford / USC
The book three statisticians from Stanford and USC made free because they wanted everyone to learn it. 290,000 people have taken the companion course on edX.
https://t.co/TusQK9FDOc
→ Computational and Inferential Thinking - Berkeley Data 8
The textbook behind Berkeley's most popular course. Data science from scratch, built to be understood without a math degree first.
https://t.co/xD2XAE49WA
→ Dive into Deep Learning - Berkeley / Amazon
Jensen Huang called it "excellent." 500 universities across 70 countries use it. Every concept runs as live code directly in the browser.
https://t.co/GJfBeDtJVO
→ Introduction to Probability - Blitzstein & Hwang, Harvard
The official textbook of Harvard's Stat 110, which has been called the best probability course ever put on YouTube. Free second edition online.
https://t.co/8dVzOUZDlH
→ The Elements of Statistical Learning - Hastie, Tibshirani, Friedman, Stanford
The graduate-level version of ISLR. Springer makes it free as a PDF. Researchers keep a copy permanently in their downloads folder.
https://t.co/PzdmjooaZW
→ MIT OCW Online Textbooks Index - 45+ books across every department
One page. Every free MIT textbook organized by subject. Algorithms, physics, economics, engineering. All open access.
https://t.co/eAjUcYzNa0
Save this before someone makes them take it down.
(They won't. But save it anyway.)
México vence a Chequia 3-0
México pasa la primera fase invicto, líder de su grupo, con 9 puntos, por primera vez en la historia. TE AMOOOO MÉXICO 🇲🇽
Y SI SI???
Aviso especial para mis seguidores de CDMX:
Compré dos boletos para el partido de México vs Chequia el 24 de junio (mañana) y se me había olvidado que ese día soy padrino de bautizo!
Si alguno de ustedes quiere asistir en mi lugar, les regalo el acceso, ya está todo pagado.
El bautizo será en la parroquia de San Agustín, en Polanco a las 4:00 pm. Sólo es cuestión de llegar y cargar al niño cuando sea necesario, la fiesta ya está pagada!