#econometrics — Public Fediverse posts

I've been reading about missForest today

MissForest—non-parametric missing value imputation for mixed-type data

https://academic.oup.com/bioinformatics/article/28/1/112/219101

https://github.com/stekhoven/missForest

Runs much faster than `{mice}` in my experience, and I like the fewer parametric assumptions.

The above article on missForest is David Stekhoven and Peter Bühlmann's most cited article.

#DataScience #statistics #academia #econometrics #Epidemiology

Alright! Today we premiered the logo of my subject Quantitative Methods 1. Ofc, it presents linear regression output. My question to you is: what's the applied problem we're talking about here? Can you guess?

Reproduction scripts: https://github.com/donotdespair/naklejki/tree/master/qm1

#qm1 #unimelb #econometrics #rstats

Alright! Today we premiered the logo of my subject Quantitative Methods 1. Ofc, it presents linear regression output. My question to you is: what's the applied problem we're talking about here? Can you guess?

Reproduction scripts: https://github.com/donotdespair/naklejki/tree/master/qm1

#qm1 #unimelb #econometrics #rstats

Alright! Today we premiered the logo of my subject Quantitative Methods 1. Ofc, it presents linear regression output. My question to you is: what's the applied problem we're talking about here? Can you guess?

Reproduction scripts: https://github.com/donotdespair/naklejki/tree/master/qm1

#qm1 #unimelb #econometrics #rstats

Maximising the value of a portfolio. Using #Variance, CoVariance and Portfolio Variance. Briefly Variance is the deviation of a stock’s return with its own average returns, Co variance on the other hand is the variance of a stock’s return with respect to another stocks’ return. http://financemetrics.scienceontheweb.net/ Using #Matrix Algebra in a 5 Company Model. #economics #econometrics

Maximising the value of a portfolio. Using #Variance, CoVariance and Portfolio Variance. Briefly Variance is the deviation of a stock’s return with its own average returns, Co variance on the other hand is the variance of a stock’s return with respect to another stocks’ return. http://financemetrics.scienceontheweb.net/ Using #Matrix Algebra in a 5 Company Model. #economics #econometrics

Maximising the value of a portfolio. Using #Variance, CoVariance and Portfolio Variance. Briefly Variance is the deviation of a stock’s return with its own average returns, Co variance on the other hand is the variance of a stock’s return with respect to another stocks’ return. http://financemetrics.scienceontheweb.net/ Using #Matrix Algebra in a 5 Company Model. #economics #econometrics

Maximising the value of a portfolio. Using #Variance, CoVariance and Portfolio Variance. Briefly Variance is the deviation of a stock’s return with its own average returns, Co variance on the other hand is the variance of a stock’s return with respect to another stocks’ return. http://financemetrics.scienceontheweb.net/ Using #Matrix Algebra in a 5 Company Model. #economics #econometrics

Maximising the value of a portfolio. Using #Variance, CoVariance and Portfolio Variance. Briefly Variance is the deviation of a stock’s return with its own average returns, Co variance on the other hand is the variance of a stock’s return with respect to another stocks’ return. http://financemetrics.scienceontheweb.net/ Using #Matrix Algebra in a 5 Company Model. #economics #econometrics

I’ve been trying to read more carefully about instrumental variables and make up my mind about when IV arguments are scientifically convincing.

Here's a tension I keep running into:

Should the scientific question alone determine the causal parameter of interest?

Or is it legitimate for the target parameter to reflect an interplay between scientific interest and the identifying assumptions we actually find tenable?

IVs can be difficult to interpret when instruments are weak, who “compliers” are is opaque, exclusion restrictions are debatable, or linear models are used in settings where the true data-generating process may be nonlinear.

On the other hand, when an entire body of (aspirationally causal) literature rests on methods that try to close backdoor paths, IVs offer a genuinely different identification strategy. That seems valuable for evidence triangulation, even if IV analyses have their criticisms.

What do you think? Are you a big IV proponent? Are you an IV critic?

When do you find IV evidence persuasive?

Some literature I've been reading & re-reading:

https://pubmed.ncbi.nlm.nih.gov/16755261/

https://academic.oup.com/ije/article/47/4/1289/3095892

https://pmc.ncbi.nlm.nih.gov/articles/PMC4285626/

https://arxiv.org/abs/2402.09332

https://arxiv.org/abs/2402.05639

#CausalInference #InstrumentalVariables #Econometrics #Statistics #DataScience #HealthPolicy

I’ve been trying to read more carefully about instrumental variables and make up my mind about when IV arguments are scientifically convincing.

Here's a tension I keep running into:

Should the scientific question alone determine the causal parameter of interest?

Or is it legitimate for the target parameter to reflect an interplay between scientific interest and the identifying assumptions we actually find tenable?

IVs can be difficult to interpret when instruments are weak, who “compliers” are is opaque, exclusion restrictions are debatable, or linear models are used in settings where the true data-generating process may be nonlinear.

On the other hand, when an entire body of (aspirationally causal) literature rests on methods that try to close backdoor paths, IVs offer a genuinely different identification strategy. That seems valuable for evidence triangulation, even if IV analyses have their criticisms.

What do you think? Are you a big IV proponent? Are you an IV critic?

When do you find IV evidence persuasive?

Some literature I've been reading & re-reading:

https://pubmed.ncbi.nlm.nih.gov/16755261/

https://academic.oup.com/ije/article/47/4/1289/3095892

https://pmc.ncbi.nlm.nih.gov/articles/PMC4285626/

https://arxiv.org/abs/2402.09332

https://arxiv.org/abs/2402.05639

#CausalInference #InstrumentalVariables #Econometrics #Statistics #DataScience #HealthPolicy

I’ve been trying to read more carefully about instrumental variables and make up my mind about when IV arguments are scientifically convincing.

Here's a tension I keep running into:

Should the scientific question alone determine the causal parameter of interest?

Or is it legitimate for the target parameter to reflect an interplay between scientific interest and the identifying assumptions we actually find tenable?

IVs can be difficult to interpret when instruments are weak, who “compliers” are is opaque, exclusion restrictions are debatable, or linear models are used in settings where the true data-generating process may be nonlinear.

On the other hand, when an entire body of (aspirationally causal) literature rests on methods that try to close backdoor paths, IVs offer a genuinely different identification strategy. That seems valuable for evidence triangulation, even if IV analyses have their criticisms.

What do you think? Are you a big IV proponent? Are you an IV critic?

When do you find IV evidence persuasive?

Some literature I've been reading & re-reading:

https://pubmed.ncbi.nlm.nih.gov/16755261/

https://academic.oup.com/ije/article/47/4/1289/3095892

https://pmc.ncbi.nlm.nih.gov/articles/PMC4285626/

https://arxiv.org/abs/2402.09332

https://arxiv.org/abs/2402.05639

#CausalInference #InstrumentalVariables #Econometrics #Statistics #DataScience #HealthPolicy

I’ve been trying to read more carefully about instrumental variables and make up my mind about when IV arguments are scientifically convincing.

Here's a tension I keep running into:

Should the scientific question alone determine the causal parameter of interest?

Or is it legitimate for the target parameter to reflect an interplay between scientific interest and the identifying assumptions we actually find tenable?

IVs can be difficult to interpret when instruments are weak, who “compliers” are is opaque, exclusion restrictions are debatable, or linear models are used in settings where the true data-generating process may be nonlinear.

On the other hand, when an entire body of (aspirationally causal) literature rests on methods that try to close backdoor paths, IVs offer a genuinely different identification strategy. That seems valuable for evidence triangulation, even if IV analyses have their criticisms.

What do you think? Are you a big IV proponent? Are you an IV critic?

When do you find IV evidence persuasive?

Some literature I've been reading & re-reading:

https://pubmed.ncbi.nlm.nih.gov/16755261/

https://academic.oup.com/ije/article/47/4/1289/3095892

https://pmc.ncbi.nlm.nih.gov/articles/PMC4285626/

https://arxiv.org/abs/2402.09332

https://arxiv.org/abs/2402.05639

#CausalInference #InstrumentalVariables #Econometrics #Statistics #DataScience #HealthPolicy

I’ve been trying to read more carefully about instrumental variables and make up my mind about when IV arguments are scientifically convincing.

Here's a tension I keep running into:

Should the scientific question alone determine the causal parameter of interest?

Or is it legitimate for the target parameter to reflect an interplay between scientific interest and the identifying assumptions we actually find tenable?

IVs can be difficult to interpret when instruments are weak, who “compliers” are is opaque, exclusion restrictions are debatable, or linear models are used in settings where the true data-generating process may be nonlinear.

On the other hand, when an entire body of (aspirationally causal) literature rests on methods that try to close backdoor paths, IVs offer a genuinely different identification strategy. That seems valuable for evidence triangulation, even if IV analyses have their criticisms.

What do you think? Are you a big IV proponent? Are you an IV critic?

When do you find IV evidence persuasive?

Some literature I've been reading & re-reading:

https://pubmed.ncbi.nlm.nih.gov/16755261/

https://academic.oup.com/ije/article/47/4/1289/3095892

https://pmc.ncbi.nlm.nih.gov/articles/PMC4285626/

https://arxiv.org/abs/2402.09332

https://arxiv.org/abs/2402.05639

#CausalInference #InstrumentalVariables #Econometrics #Statistics #DataScience #HealthPolicy

Hi @geneshackman ,

#Gretl has a GUI (incl. an editor + terminal). You can steer gretl it via the GUI or via pure scripting.

Website: https://gretl.sourceforge.net/

Additional resources & links : https://github.com/gretl-project/material-on-gretl

Link to manual and references:
https://gretl.sourceforge.net/#man

Let us know if you need more information.

#econometrics #statistics #datascience

Hi @geneshackman ,

#Gretl has a GUI (incl. an editor + terminal). You can steer gretl it via the GUI or via pure scripting.

Website: https://gretl.sourceforge.net/

Additional resources & links : https://github.com/gretl-project/material-on-gretl

Link to manual and references:
https://gretl.sourceforge.net/#man

Let us know if you need more information.

#econometrics #statistics #datascience

Hi @geneshackman ,

#Gretl has a GUI (incl. an editor + terminal). You can steer gretl it via the GUI or via pure scripting.

Website: https://gretl.sourceforge.net/

Additional resources & links : https://github.com/gretl-project/material-on-gretl

Link to manual and references:
https://gretl.sourceforge.net/#man

Let us know if you need more information.

#econometrics #statistics #datascience

Hi @geneshackman ,

#Gretl has a GUI (incl. an editor + terminal). You can steer gretl it via the GUI or via pure scripting.

Website: https://gretl.sourceforge.net/

Additional resources & links : https://github.com/gretl-project/material-on-gretl

Link to manual and references:
https://gretl.sourceforge.net/#man

Let us know if you need more information.

#econometrics #statistics #datascience

Identification and Semiparametric Estimation of Conditional Means from Aggregate Data
https://arxiv.org/pdf/2509.20194
Ecological inference is the challenge of estimating subgroup behavior using only aggregate data like geographic averages. This paper introduces a new semiparametric method using debiased #machineLearning to improve estimate accuracy. The approach formalizes identifying assumptions and uses many covariates to minimize statistical bias. Tools for sensitivity analysis and unit-level estimation ensure results remain #robust under varying conditions. Tests on voting and pollution data show this method outperforms traditional models in precision and speed.
#Rstats package: https://corymccartan.com/seine/
#ecologicalinference #machinelearning #statistics #econometrics

Analysis of Financial Time Series 3rd Edition by Ruey S. Tsay (PDF)
Author: Ruey S. Tsay
File Type: PDF
Download at https://sci-books.com/analysis-of-financial-time-series-3rd-edition-0470414359/
#Econometrics, #RueyS.Tsay

Analysis of Financial Time Series 3rd Edition by Ruey S. Tsay (PDF)
Author: Ruey S. Tsay
File Type: PDF
Download at https://sci-books.com/analysis-of-financial-time-series-3rd-edition-0470414359/
#Econometrics, #RueyS.Tsay

Analysis of Financial Time Series 3rd Edition by Ruey S. Tsay (PDF)
Author: Ruey S. Tsay
File Type: PDF
Download at https://sci-books.com/analysis-of-financial-time-series-3rd-edition-0470414359/
#Econometrics, #RueyS.Tsay

Analysis of Financial Time Series 3rd Edition by Ruey S. Tsay (PDF)
Author: Ruey S. Tsay
File Type: PDF
Download at https://sci-books.com/analysis-of-financial-time-series-3rd-edition-0470414359/
#Econometrics, #RueyS.Tsay

Analysis of Financial Time Series 3rd Edition by Ruey S. Tsay (PDF)
Author: Ruey S. Tsay
File Type: PDF
Download at https://sci-books.com/analysis-of-financial-time-series-3rd-edition-0470414359/
#Econometrics, #RueyS.Tsay

Don't miss today's #DiSCourseSeminar with Vaarun Vijairaghavan from the University of Calgary, Canada, at 12:00 (CET). You can join onsite at the DiSC, Innrain 15, 6020 Innsbruck or remotely via Big Blue Button: https://webconference.uibk.ac.at/b/car-aab-cxf-o81

Topic: Fair Play for Fair Pay: Fighting Digital Piracy through Revenue Sharing

#InformationSystems
#ResearchTalk
#DigitalPiracy
#CopyrightInfringement
#Modeling
#Econometrics

Gretl version 2026a is now available. Key updates include:

- RNG: Mersenne Twister replaced by xoshiro256+.
- Estimation: QR decomposition for binary logit/probit Hessian stability.
- Commands: New --head/--tail for 'print'.
- Accessors: Improved $coeff and $stderr for multiple-tau quantreg.
- Bug fixes: Resolved crashes in mat2list() and kdsmooth(); fixed MPI issues in regls().

Changelog: https://gretl.sourceforge.net/ChangeLog.html#v2026a

#Gretl #Econometrics #Statistics #DataScience #OpenSource

Gretl version 2026a is now available. Key updates include:

- RNG: Mersenne Twister replaced by xoshiro256+.
- Estimation: QR decomposition for binary logit/probit Hessian stability.
- Commands: New --head/--tail for 'print'.
- Accessors: Improved $coeff and $stderr for multiple-tau quantreg.
- Bug fixes: Resolved crashes in mat2list() and kdsmooth(); fixed MPI issues in regls().

Changelog: https://gretl.sourceforge.net/ChangeLog.html#v2026a

#Gretl #Econometrics #Statistics #DataScience #OpenSource

Gretl version 2026a is now available. Key updates include:

- RNG: Mersenne Twister replaced by xoshiro256+.
- Estimation: QR decomposition for binary logit/probit Hessian stability.
- Commands: New --head/--tail for 'print'.
- Accessors: Improved $coeff and $stderr for multiple-tau quantreg.
- Bug fixes: Resolved crashes in mat2list() and kdsmooth(); fixed MPI issues in regls().

Changelog: https://gretl.sourceforge.net/ChangeLog.html#v2026a

#Gretl #Econometrics #Statistics #DataScience #OpenSource

Gretl version 2026a is now available. Key updates include:

- RNG: Mersenne Twister replaced by xoshiro256+.
- Estimation: QR decomposition for binary logit/probit Hessian stability.
- Commands: New --head/--tail for 'print'.
- Accessors: Improved $coeff and $stderr for multiple-tau quantreg.
- Bug fixes: Resolved crashes in mat2list() and kdsmooth(); fixed MPI issues in regls().

Changelog: https://gretl.sourceforge.net/ChangeLog.html#v2026a

#Gretl #Econometrics #Statistics #DataScience #OpenSource

Gretl version 2026a is now available. Key updates include:

- RNG: Mersenne Twister replaced by xoshiro256+.
- Estimation: QR decomposition for binary logit/probit Hessian stability.
- Commands: New --head/--tail for 'print'.
- Accessors: Improved $coeff and $stderr for multiple-tau quantreg.
- Bug fixes: Resolved crashes in mat2list() and kdsmooth(); fixed MPI issues in regls().

Changelog: https://gretl.sourceforge.net/ChangeLog.html#v2026a

#Gretl #Econometrics #Statistics #DataScience #OpenSource

#DigitalIndependence #diday for #SocialScience #researchers:

#Rstats #Jamovi #Python <-- #SPSS #Stata

#LibreOffice #LaTeX #Typst editors #Gnumeric <-- #MicrosoftOffice

#CollaboraOnline <-- #GoogleDocs #GoogleWorkspace #Microsoft365

#Zotero <-- #Endnote #Mendeley #Citavi

#ODK #KoboToolbox #LimeSurvey <-- #Qualtrics #SurveyMonkey etc.

#requal #Taguette <-- #MaxQDA #AtlasTI #NVivo

#PoliticalScience #Economics #Statistics #Econometrics #QualitativeResearch #Evaluation #QuantitativeResearch

#DigitalIndependence #diday for #SocialScience #researchers:

#Rstats #Jamovi #Python <-- #SPSS #Stata

#LibreOffice #LaTeX #Typst editors #Gnumeric <-- #MicrosoftOffice

#CollaboraOnline <-- #GoogleDocs #GoogleWorkspace #Microsoft365

#Zotero <-- #Endnote #Mendeley #Citavi

#ODK #KoboToolbox #LimeSurvey <-- #Qualtrics #SurveyMonkey etc.

#requal #Taguette <-- #MaxQDA #AtlasTI #NVivo

#PoliticalScience #Economics #Statistics #Econometrics #QualitativeResearch #Evaluation #QuantitativeResearch

#DigitalIndependence #diday for #SocialScience #researchers:

#Rstats #Jamovi #Python <-- #SPSS #Stata

#LibreOffice #LaTeX #Typst editors #Gnumeric <-- #MicrosoftOffice

#CollaboraOnline <-- #GoogleDocs #GoogleWorkspace #Microsoft365

#Zotero <-- #Endnote #Mendeley #Citavi

#ODK #KoboToolbox #LimeSurvey <-- #Qualtrics #SurveyMonkey etc.

#requal #Taguette <-- #MaxQDA #AtlasTI #NVivo

#PoliticalScience #Economics #Statistics #Econometrics #QualitativeResearch #Evaluation #QuantitativeResearch

#DigitalIndependence #diday for #SocialScience #researchers:

#Rstats #Jamovi #Python <-- #SPSS #Stata

#LibreOffice #LaTeX #Typst editors #Gnumeric <-- #MicrosoftOffice

#CollaboraOnline <-- #GoogleDocs #GoogleWorkspace #Microsoft365

#Zotero <-- #Endnote #Mendeley #Citavi

#ODK #KoboToolbox #LimeSurvey <-- #Qualtrics #SurveyMonkey etc.

#requal #Taguette <-- #MaxQDA #AtlasTI #NVivo

#PoliticalScience #Economics #Statistics #Econometrics #QualitativeResearch #Evaluation #QuantitativeResearch

#DigitalIndependence #diday for #SocialScience #researchers:

#Rstats #Jamovi #Python <-- #SPSS #Stata

#LibreOffice #LaTeX #Typst editors #Gnumeric <-- #MicrosoftOffice

#CollaboraOnline <-- #GoogleDocs #GoogleWorkspace #Microsoft365

#Zotero <-- #Endnote #Mendeley #Citavi

#ODK #KoboToolbox #LimeSurvey <-- #Qualtrics #SurveyMonkey etc.

#requal #Taguette <-- #MaxQDA #AtlasTI #NVivo

#PoliticalScience #Economics #Statistics #Econometrics #QualitativeResearch #Evaluation #QuantitativeResearch

Asymptotic Chaos Expansions in Finance: Theory and Practice (Springer Finance) 2014th Edition by David Nicolay (PDF)
Author: David Nicolay
File Type: PDF
Download at https://sci-books.com/asymptotic-chaos-expansions-in-finance-theory-and-practice-springer-finance-2014th-edition-b00q4xf2y6/
#Econometrics, #DavidNicolay

Asymptotic Chaos Expansions in Finance: Theory and Practice (Springer Finance) 2014th Edition by David Nicolay (PDF)
Author: David Nicolay
File Type: PDF
Download at https://sci-books.com/asymptotic-chaos-expansions-in-finance-theory-and-practice-springer-finance-2014th-edition-b00q4xf2y6/
#Econometrics, #DavidNicolay

Asymptotic Chaos Expansions in Finance: Theory and Practice (Springer Finance) 2014th Edition by David Nicolay (PDF)
Author: David Nicolay
File Type: PDF
Download at https://sci-books.com/asymptotic-chaos-expansions-in-finance-theory-and-practice-springer-finance-2014th-edition-b00q4xf2y6/
#Econometrics, #DavidNicolay

Asymptotic Chaos Expansions in Finance: Theory and Practice (Springer Finance) 2014th Edition by David Nicolay (PDF)
Author: David Nicolay
File Type: PDF
Download at https://sci-books.com/asymptotic-chaos-expansions-in-finance-theory-and-practice-springer-finance-2014th-edition-b00q4xf2y6/
#Econometrics, #DavidNicolay

Asymptotic Chaos Expansions in Finance: Theory and Practice (Springer Finance) 2014th Edition by David Nicolay (PDF)
Author: David Nicolay
File Type: PDF
Download at https://sci-books.com/asymptotic-chaos-expansions-in-finance-theory-and-practice-springer-finance-2014th-edition-b00q4xf2y6/
#Econometrics, #DavidNicolay

🎉 Gretl 2025c is here!
Exciting updates to your favorite econometrics toolkit! Version 2025c brings powerful new features and improvements:

✨ New Features:
Gibbs sampler command for Bayesian analysis is available now!

🚀 Performance & Quality:
Faster forward stepwise regression

🎨 GUI Enhancements:
Better dbnomics search integration
Improved dark theme support

Full changelog:

https://gretl.sourceforge.net/ChangeLog.html

#gretl #econometrics #opensource #statistics #datascience #economics #timeseries

🎉 Gretl 2025c is here!
Exciting updates to your favorite econometrics toolkit! Version 2025c brings powerful new features and improvements:

✨ New Features:
Gibbs sampler command for Bayesian analysis is available now!

🚀 Performance & Quality:
Faster forward stepwise regression

🎨 GUI Enhancements:
Better dbnomics search integration
Improved dark theme support

Full changelog:

https://gretl.sourceforge.net/ChangeLog.html

#gretl #econometrics #opensource #statistics #datascience #economics #timeseries

🎉 Gretl 2025c is here!
Exciting updates to your favorite econometrics toolkit! Version 2025c brings powerful new features and improvements:

✨ New Features:
Gibbs sampler command for Bayesian analysis is available now!

🚀 Performance & Quality:
Faster forward stepwise regression

🎨 GUI Enhancements:
Better dbnomics search integration
Improved dark theme support

Full changelog:

https://gretl.sourceforge.net/ChangeLog.html

#gretl #econometrics #opensource #statistics #datascience #economics #timeseries

🎉 Gretl 2025c is here!
Exciting updates to your favorite econometrics toolkit! Version 2025c brings powerful new features and improvements:

✨ New Features:
Gibbs sampler command for Bayesian analysis is available now!

🚀 Performance & Quality:
Faster forward stepwise regression

🎨 GUI Enhancements:
Better dbnomics search integration
Improved dark theme support

Full changelog:

https://gretl.sourceforge.net/ChangeLog.html

#gretl #econometrics #opensource #statistics #datascience

AI’s $1 trillion bet - is it an #AI bubble or dot-com bust? Global data‑center capital expenditure to power AI is projected to rise from roughly $430 billion this year to over $1.1 trillion by 2029 (which is equal to the GDP of the Netherlands). Why it matters:
We’re witnessing an infrastructure boom that echoes a familiar pattern in tech history but the question is whether it’s building toward lasting transformation or racing toward collapse.

Capital is pouring into data centers, cooling systems, power infrastructure, and networks at a staggering pace. Yet most AI applications haven’t proven they can generate sustainable revenue at scale.

BTW, this article is a good one, even though it bears the hallmarks of some AI input. I’m tired of hearing about #AISlop from folks who don’t even read materials to determine if they deliver meaningful content. I use AI as my research & brainstorming asst. It works.

https://www.jeffbullas.com/ai-bubble-or-dot-com-bust/ #ArtificialIntelligence #economy #econometrics #markets #finance #technology

👽 There it is! 👾 Our new and shiny paper for the Journal of Econometrics! 🤖
https://doi.org/10.1016/j.jeconom.2025.106107 🚀

In this paper:
✅ we provide general conditions for partial identification of Structural VARs through heteroskedasticity
✅ we show that it's great for analysing fiscal policy effects on the economy
✅ it's the methodological paper for my bsvars package

👇

#econometrics #1000hofComputations #itwaswothit

CPC-CG members Professor Jackie Wahba OBE and Professor Athina Vlachantoni have been announced as #REF 2029 Sub-panel members for #Economics and #Econometrics, and #SocialWork and #SocialPolicy, respectively.

They join CPC-CG Director Professor Jane Falkingham CBE who is Chair of Main Panel C– #SocialSciences. Full story: https://www.cpc.ac.uk/news/latest_news/?action=story&id=847

#researchexcellenceframework #demography #research #socialscience #ageing #migration #economist

從檢定發現美國失業率是廣義極值分配特性。同時，Gumbel,type I有機率密度函數，真實告訴你美國失業率的機率模型，而不是出一張圖代表存在機率模型。

以上這些方法都是超越傳統AI的數據分析方法，真正從數據本質出發打造精確統計模型，解決通用模型無法捕捉真實數據規律的難題，通過自動化建模過程揭示隱藏的數學規律。

你學的是落在哪種層次呢？

直線建模能做到，當然非線性的人工智慧自動化建模同樣能做到。數據規律的數學化、自動化(更新+建模+模擬)、強大而直觀的統計分析工具集成，統計學習達成。

其中一種非線性建模：https://x.com/meiyulee357/status/1963281302423241168

@academicchatter @econometrics @ida

#AI #數據分析 #失業 #美國 #經濟 #計量經濟 #modelling #econometrics #unemployment #Statistics #USA #dataanalysis

如何建構美國失業率的機率模型？機率模型最後要有數學式顯示，不能只是圖形。

1) 直方圖？別想了，沒有數學式。只是圖像視覺化，不是數據分析，也不是人工智慧該有的數據模型。【不合格】
2) 用直方圖的組中點和對應機率值？11組可以使用AI-based piecewise linear regression method的結果是兩段直線。整體的R2達73%。【合格】
3) 建立更多分組的直方圖產生組中點與機率值。運用AI-based piecewise linear regressin method，產生9段直線。整體的R2達93%。【合格】
4) 運用適合度檢定，檢定45種機率分配？發現美國失業率的機率模型服從Gumbel,type I(a=0.68,b=27.82)。根據a值升序模擬產生條件機率分配。【合格】

@academicchatter @econometrics @ida

#AI #數據分析 #失業 #美國 #經濟 #計量經濟 #modelling #econometrics #Statistics #artificialintelligence

美國貨幣供給量的增加，維持近20個月的穩定增長。從2023年12月到2025年7月，平均每月增加673.29699億美元。2025年3月接近2022年3月的金額，4月突破2022年3月的金額，6與7月的貨幣供給量再次超過2022年3月的金額。

#美國 #經濟 #財經 #貨幣 #M2 #AI #MathAI #AI數據分析 #economy #economics #econometrics #econdon #usa