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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

arstechnica.com/science/2024/0

Ars Technica · Climate damages by 2050 will be 6 times the cost of limiting warming to 2°Study tracks the past costs of climate events and projects them into the future.

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

#introduction I am a Prof at Oxford.

My research lies at the intersection of several fields, including #econometrics, #machinelearning, #labor, #inequality, #AI.

Current interests include the combination of ML theory and welfare economics, adaptive experimental design, pre-analysis plans, #jobguarantee programs, and #basicincome.

I teach foundations of ML to economists.I post links to papers, talks, long-form articles etc.

Website: maxkasy.github.io/home/

Maximilian KasyMaximilian KasyResearch on machine learning, experimental design, economic inequality, and optimal policy

Hello all!

I'm a PhD student at King's Business School, King's College London (#KCL) and also affiliated with the Qatar Centre for Global Banking and Finance (#QCGBF).

I'm interested in applied #timeseries, #businesscycle analysis and lately, I've been studying #regimeswitching models.

My homepage is szlatanos.github.io/

Stylianos ZlatanosSTYLIANOS ZLATANOS | Stylianos Zlatanos

Re-#introduction time

By profession, I'm an international political economist #InternationalPoliticalEconomy #IPE) at a London university. Substantively, my research spans #PoliticalScience #Economics #InternationalBusiness and obviously #PoliticalEconomy. Analytically, it's mostly #Econometrics and what the kids call #DataScience but I also do other things.

I toot about #Politics, #Policy and #Academia, and personal stuff like the interests listed in my profile. Cheers!