#econometrics

Exciting news! **v1.0** of my PairPlot package for #Gretl is now available!

This release includes important bug fixes and improvements to enhance your experience with scatterplot matrices using gnuplot in Gretl.

Check out the details and get started on GitHub: [Pairplot repository](https://github.com/atecon/pairplot)

#DataScience #Statistics #Econometrics #OpenSource #Gretl

Announcing the release of **v0.5** of my kmeans package for #Gretl.

This update includes some improvement and a bugfix.

Explore the new features and improvements on GitHub: [kmeans repository](https://github.com/atecon/kmeans)

#DataScience #Statistics #Econometrics #OpenSource #gretl

The dedication and passion in this cohort are **next level**!

Looking forward to an **exciting** journey ahead!

#AppliedConformalPrediction #DataScience #MachineLearning #GlobalLearning #Finance #HFT #SoftwareDevelopment #Econometrics #AI #Research

#Finance #Banking #Innovation #Italy #RiskManagement #AI #MachineLearning #Econometrics #FinancialTechnology #BankOfItaly

Check out the repo [GitHub: EconAIorg/LPCI](https://github.com/EconAIorg/LPCI)
Read the paper [arXiv](https://arxiv.org/abs/2310.02863)

#AI #Econometrics #MachineLearning #ConformalPrediction #PanelData

Large Language Models: An Applied Econometric Framework https://d.repec.org/n?u=RePEc:arx:papers:2412.07031&r=&r=cmp
"Altogether our results suggest that the excitement around the empirical uses of LLMs is warranted, provided researchers guard against training leakage by using open-source LLMs in prediction problems and collect benchmark data in estimation problems."

An earlier abstract read: "The only way to ensure no training leakage is to use open-source LLMs with documented training data and published weights. The only way to deal with #LLM measurement error is to collect validation data and model the error structure. A corollary is that if such conditions can't be met for a candidate LLM application, our strong advice is: don't."
#Econometrics #ML #AI #economics

New kmeans v0.4 package for #Gretl released by @atecon .

Featuring:
• New kmeans_screeplot() function to find optimal cluster numbers
• Improved plot styling with Gretl's default values
• Fixed distance measure handling in GUI

Details: https://gretl.sourceforge.net/current_fnfiles/kmeans.gfn

#Statistics #DataScience #DataAnalysis #OpenSource #econometrics

New #paper out: « The impact of the #COVID19 pandemic on women’s contribution to public code » (Empir. Softw. Eng. 30(1): 25 (2025)) where we establish, using #econometrics techniques and relying on the @swheritage archive, that the pandemic disproportionately impacted women's ability to contribute to the development of public code, relatively to men. #Openaccess preprint at: https://hal.science/hal-04716803/

With A. Casanueva, D.Rossi, and @Zimm_i48

#SoftwareEngineering #opensource

#introduction

I am economist & political scientist (PhD) and conduct research on #SocialRiskManagement #ClimateChange #GlobalDevelopment #SocialProtection #PublicHealth #CooperativeStudies.

My current focus is on the topics #ClimateChangeAdaptation, #DisasterRiskManagement, #RuralDevelopment, #HealthCareFinancing, #HealthSystem‎‎s.

#ResearchMethods: #MixedMethods & #MethodIntegration, #Quantitative & #Qualitative, #RigorousImpactEvaluation, #Causality, #Econometrics, #Evaluation.

Exciting news, #Gretl users! The latest issue of Computational Statistics is out, featuring 5 articles on our favorite opensource econometrics & statistics software!

These articles explore advanced modeling techniques such as
- Bayesian VARs
- Time-varying parameter model

It's fantastic to see Gretl gaining recognition in the academic community!

Check out the full issue here:
https://link.springer.com/journal/180/volumes-and-issues/39-7

#DataScience #econometrics #statistics #opensource #economics #econtwitter

Around Cambridge (MA) next Friday? Lots of experience with Econometrics / Education Research / Survey Analysis in R?

Come give or attend our upcoming Lightning Talks with Harvard Kennedy School Data + Donuts!

See all the details and sign up here:
https://rug-at-hdsi.org/upcoming_events/2024-11-15-lightning-talks.html

One speaker will win a Raspberry Pi 4B!

#rstats #Econometrics #government #DataScience #Programming #Talk #academia #DigitalHumanities

The US Federal Reserve's FRB/US model is now available in R! Guest post by Andrea Luciani (Bank of Italy, Directorate General for Economics, Statistics and Research), maintainer of the #bimets package. Learn how to perform econometric exercises using #R.

https://r-consortium.org/posts/us-federal-reserve-quarterly-model-in-r/

#Econometrics #FedEconomy #RStats #DataScience #PythonToR

@jeanas I won’t leak the author, but my guess is that you can pick any #econometrics paper studying policy impact on CO2 emissions and you will find the same. Pick for example that one where you’ll find the following equation in the Supplementary Information: https://www.science.org/doi/10.1126/science.adl6547

Without logarithms the equation would make sense, with logarithms it’s just bullshit.

The author also pointed out that Bayer and Aklin (2020, PNAS) use the same methodology.

The Great Divide (btw applied and theoretical #econometrics )
https://dlm-econometrics.blogspot.com/2024/06/the-great-divide.html
I can't say anything about it wrt econometrics, but found it interesting on a general level. For me, the question that came up was about the uptake of new methods, qualitative or quantitative 1/

Exciting PhD opportunities (please boost)

I have up to three positions for funded PhD students at the University of St.Gallen. Applicants should be interested in one (or more) of the following topics:
- Machine learning in taxation
- Understanding preferences on taxation
- Decision theory and behavioral finance

More information is in the link below.

#PhD #Economics #Finance #Econometrics #MachineLearning #Taxation #DecisionTheory #BehavioralFinance

https://jobs.unisg.ch/offene-stellen/phd-student-and-research-associate-positions-in-economics-and-finance-m-f-d/ed2a5558-3bef-4697-aaa9-b1a8e9dd86fd

New article in Nature estimates huge potential climate damages based on econometric estimates of past climate damages.

What do statisticians think about whether the methodology used makes sense? Can any such statistical approach have enough power to estimate past damages from a zillion confounding variables?

Link to study in below popular article

#Econometrics #Statistics #Climate #ClimateChange #ClimateCrisis

https://arstechnica.com/science/2024/04/climate-damages-by-2050-will-be-6-times-the-cost-of-limiting-warming-to-2/

The fourth down thing really has come pervasive. I'm not familiar with the model used, but there are some serious data issues. Selection and endogeneity are enormous problems that may not be accounted for. Admittedly some of these issues have gone away as more and more coaches go for it on 4th down, but still. https://www.wsj.com/sports/football/detroit-lions-fourth-down-super-bowl-9b1555d5?mod=hp_lead_pos8 #nfl #4thdown #econometrics

Dive into the world of #Statistics & #Econometrics with Gretl! Learn how to conduct non-parametric & parametric tests for differences between variables or units. Unleash your #DataScience potential. Check out our new guide: https://github.com/gretl-project/material-on-gretl/wiki/Statistics #Gretl

Introducing LongMemory.jl: A Julia Package for Long Memory Time Series Analysis

I am happy to announce that after several months of getting to understand the language better, I have finally published my first Julia registered package: LongMemory.jl. This package is the result of my research on long memory time series analysis, which is a fascinating topic in econometrics and statistics. Long memory models are useful for capturing the persistence and dependence of many real-world phenomena, such as inflation, interest rates, volatility, network traffic, and environmental data.

LongMemory.jl makes it easy to generate, estimate, and forecast long memory models in Julia. It supports various types of models, such as fractional differencing, cross-sectional aggregation, and stochastic duration shocks. It also provides functions for testing the presence of long memory, computing the Hurst exponent, and simulating long memory processes. The package is fully documented and includes classical data examples, such as the Nile River minima.

The package can be installed easily from the Julia general registry. I have prepared a short video that shows how to install the package and generate long memory diagnostics plots for the Nile River minima dataset. The Nile River minima is a famous example of a long memory time series.

I hope you find LongMemory.jl useful and practical. I welcome any feedback, suggestions, or contributions to improve the package. You can contact me or open an issue on GitHub. Thank you for your interest and feedback!

#julialang #programming #programmingjourney #longmemory #timeseriesanalysis #timeseries #econometrics #statistics @julialanguage@bird.makeup @julialanguage@mastodon.social

2/ Meet "tsplots" – the time-series functionality of the "scatters" command. Play with string-valued series and the '*' operator for string repetition. Also, explore the new corresp() function. Embrace the new possibilities! #Gretl #Econometrics #Statistics #DataScience #Econtwitter