Carlos Bruno

Economista conductual y de desarrollo económico.

[Carlos Bruno @BrunoDel2c]

@ocampo_emilio no tienes argumentos, no tienes evidencias. Dices que la evidencia empírica internacional te respalda, y eso no es cierto. MENTIROSO

[Carlos Bruno @BrunoDel2c]

@jhonnyvasquez61 @Simbelmynes Que boludo...

[Carlos Bruno @BrunoDel2c]

@RafaelMart22555 @Simbelmynes O sea que la mayoria eran contrabandistas, cuanto daño les hizo el avelino y el tik tok

[Mehmet Songur @mehmetsongur_]

“Ekonomistlerin Bilmesi Gereken Makine Öğrenme Yöntemleri”

Susan Athey ve Guido W. Imbens

🔗https://t.co/cgNQepGYvV

[NBER @nberpubs]

Describing three stages at which AI enters the measurement pipeline—discovery, construct definition, and observation—and what each demands of researchers, from Melissa Dell and @asheshrambachan https://t.co/3fdww0gAdR

[Mehmet Songur @mehmetsongur_]

Google, "Yapay Zeka ve Ekonomi Araştırma Programı"nı duyurdu

Yapay zekanın ekonomik etkilerini modellemek üzere Nobel ödüllü Philippe Aghion ve Ajay Agrawal gibi önemli iktisatçıları araştırma kadrosuna kattı.

Bu ekibin öncelikli hedefi, istihdamdan büyümeye kadar yapay zekanın yaratacağı yapısal şokları haritalandırmak olacak.

Beni asıl heyecanlandıran kısım ise işin veri boyutu. Gecikmeli geleneksel makro veriler yerine, gerçek zamanlı büyük veri ve makine öğrenmesiyle yapılacak bu yeni nesil analizleri yakından takip etmemiz şart.

[Joachim Schork @JoachimSchork]

Many people think that Propensity Score Matching (PSM) is finished once the matching has been performed.

In reality, one of the most important steps comes afterward: checking whether the matching actually worked.

The cobalt package is one of the best tools in R for assessing covariate balance after matching. It provides a wide range of visualizations and diagnostics, including:

🔹 Love plots showing standardized mean differences before and after matching
🔹 Distributional balance plots for individual covariates
🔹 Propensity score distribution plots
🔹 Variance ratios and many other balance statistics

These diagnostics help you determine whether the treated and control groups have become sufficiently comparable. If not, you may need to adjust your matching approach before interpreting treatment effects.

The figure below shows a few examples of the balance diagnostics that can be created with cobalt.

I have just released a new module in the Statistics Globe Hub in which I explain how to perform Propensity Score Matching in R. The module covers the complete PSM workflow, including propensity score estimation, nearest neighbor matching, balance assessment with the cobalt package, treatment effect estimation, and practical examples using fully reproducible R code.

The Statistics Globe Hub is an ongoing learning program focused on practical skills in statistics, data science, AI, and programming with R and Python.

More information about the Hub: https://t.co/NA2b7UAXJ4

#RStats #RProgramming #DataScience #Statistics #CausalInference #Analytics #StatisticsGlobeHub

[J-PAL North America @JPAL_NA]

Want to know the impact of your #RCT? Check out our data analysis resources 📖 for an overview of how to analyze data to estimate causal impact #EconTwitter https://t.co/B6nw2JMOrP

[Carlos Bruno @BrunoDel2c]

@YerkoGarafulic da verguenza y un poco de asco...

[Carlos Bruno @BrunoDel2c]

@jcpereirasta Dudo que alguien con 3 dedos de frente lo considere intelectual, mad allá de La Paz...ni enterados quien es el mequetrefe.

[Carlos Bruno @BrunoDel2c]

@BrockmannRobert Un poco boludo de tu parte

[Máximo @MaximoQuintia]

Susan Athey acerca del overlap entre Econometría & Machine Learning:

https://t.co/os76Qi1BIc

Muy recomendable para quienes estén comenzando a incursionar en este mundo.

[Mehmet Songur @mehmetsongur_]

Susan Athey tarafından yazılan "The Impact of Machine Learning on Economics" başlıklı çalışma makine öğrenmesinin ekonomi alanında kullanımına ilişkin değerli çalışmalardan birisidir.
Aşağıdaki linkten ulaşabilirsiniz.

🔗 https://t.co/8azYYRXlC0

[International and Monetary Economics Network @int_mon_econ]

Very valuable!

"Macroeconometrics" by Alessia Paccagnini.

"Macroeconometrics provides a rigorous yet accessible guide to the tools used to analyse dynamic economic systems in a rapidly evolving empirical environment. The book takes readers from the foundations of univariate time series analysis to the multivariate and structural methods that define modern macroeconometrics. It covers core topics such as stationarity, unit roots, cointegration, ARIMA and GARCH models, VARs, local projections, shock identification, Bayesian methods, and DSGE models, while also introducing recent advances in high-dimensional data, machine learning, nonlinearities, mixed-frequency analysis, quantile methods, Growth-at-Risk, and multi-country policy modelling. Structured as a progressive learning journey, it combines theoretical explanation with practical guidance, empirical applications, summary sections, key equations, exercises, and companion code."

https://t.co/H29gDpPI2T

[mauforonda @mauforonda]

Hace días reapareció GeoBolivia, el portal oficial de datos geográficos, y aproveché para archivarlo todo. Puedes explorar y descargar lo que quieras, todo estará ahí para siempre. Ya tenemos 6,674 datasets sobre todos los temas que puedas imaginar.

https://t.co/yZire5uBac

🌳

[Carlos Bruno @BrunoDel2c]

@amilcarbarral Vaya chiste...como nos reímos.

[Peter Hull @instrumenthull]

our new JEP on judge/exminar/leniency IVs is out today! Check it out 👇

[Carlos Bruno @BrunoDel2c]

@Chichisiles Bueno, para algo hay traductores.

[Cambridge Economics @CambUP_Econ]

New! Solis-Garcia’s Macroeconomic Modeling | Preparing students for graduate study, the text introduces dynamic stochastic general equilibrium (DSGE) models through equilibrium theory.
Find out more: ☑️ https://t.co/zysnhJtRl6

[Carlos Bruno @BrunoDel2c]

@ZamoraLiebers Como diría Lara, es bueno saberlo