#econometrics — Public Fediverse posts

⭐ It's official now! ✨ Our latest paper is out in the Journal of Econometrics 💫 https://doi.org/10.1016/j.jeconom.2025.106107

⭐ Partial identification of structural vector autoregressions with non-centred stochastic volatility 💛

#econometrics #bsvars #identification #verification #fiscal

⭐ It's official now! ✨ Our latest paper is out in the Journal of Econometrics 💫 https://doi.org/10.1016/j.jeconom.2025.106107

⭐ Partial identification of structural vector autoregressions with non-centred stochastic volatility 💛

#econometrics #bsvars #identification #verification #fiscal

⭐ It's official now! ✨ Our latest paper is out in the Journal of Econometrics 💫 https://doi.org/10.1016/j.jeconom.2025.106107

⭐ Partial identification of structural vector autoregressions with non-centred stochastic volatility 💛

#econometrics #bsvars #identification #verification #fiscal

⭐ It's official now! ✨ Our latest paper is out in the Journal of Econometrics 💫 https://doi.org/10.1016/j.jeconom.2025.106107

⭐ Partial identification of structural vector autoregressions with non-centred stochastic volatility 💛

#econometrics #bsvars #identification #verification #fiscal

📊 Turning data into business insights with analytics & AI.

🎓 Master Business Analytics & Econometrics (M.Sc.)

✨ Analytics, AI & machine learning
✨ Hands-on skills in R, Python & econometrics
✨ International career opportunities in data & consulting

🔗https://uni.koeln/R86FF

#BusinessAnalytics #Econometrics #UniversityOfCologne

📊 Turning data into business insights with analytics & AI.

🎓 Master Business Analytics & Econometrics (M.Sc.)

✨ Analytics, AI & machine learning
✨ Hands-on skills in R, Python & econometrics
✨ International career opportunities in data & consulting

🔗https://uni.koeln/R86FF

#BusinessAnalytics #Econometrics #UniversityOfCologne

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

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

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

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

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

Fun fact: the Europe, Australasia, and Far East index of stock markets includes precisely four Asian countries: Israel, Singapore, Hong Kong, and Japan. Those are the only countries with sufficiently large and stable stock markets that the economists at MSCI put them in the same category as Germany or New Zealand. #investing #econometrics

Fun fact: the Europe, Australasia, and Far East index of stock markets includes precisely four Asian countries: Israel, Singapore, Hong Kong, and Japan. Those are the only countries with sufficiently large and stable stock markets that the economists at MSCI put them in the same category as Germany or New Zealand. #investing #econometrics

Fun fact: the Europe, Australasia, and Far East index of stock markets includes precisely four Asian countries: Israel, Singapore, Hong Kong, and Japan. Those are the only countries with sufficiently large and stable stock markets that the economists at MSCI put them in the same category as Germany or New Zealand. #investing #econometrics

Fun fact: the Europe, Australasia, and Far East index of stock markets includes precisely four Asian countries: Israel, Singapore, Hong Kong, and Japan. Those are the only countries with sufficiently large and stable stock markets that the economists at MSCI put them in the same category as Germany or New Zealand. #investing #econometrics

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

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

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

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

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

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

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

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

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

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