statsmodels: Statistical Modeling and Econometrics in Python ##python
statsmodels: Statistical Modeling and Econometrics in Python #python
Statsmodels: statistical modeling and econometrics in Python - statsmodels/statsmodels
github.com
January 10, 2025 at 4:00 AM
statsmodels: Statistical Modeling and Econometrics in Python ##python
Time Series Analysis with StatsModels
Video from my PyData Global tutorial is up now:
www.youtube.com/watch?v=foMb...
Video from my PyData Global tutorial is up now:
www.youtube.com/watch?v=foMb...
Allen Downey - Time Series Analysis with StatsModels | PyData Global 2024
YouTube video by PyData
www.youtube.com
April 24, 2025 at 2:11 PM
Time Series Analysis with StatsModels
Video from my PyData Global tutorial is up now:
www.youtube.com/watch?v=foMb...
Video from my PyData Global tutorial is up now:
www.youtube.com/watch?v=foMb...
Our py-econometrics org has a new package: `maketables` is now installable from PyPi! You can create regression tables for PyFixest, statsmodels, linearmodels, and save them as html, latex, or word.
github: github.com/py-econometr...
docs: py-econometrics.github.io/maketables/
github: github.com/py-econometr...
docs: py-econometrics.github.io/maketables/
November 20, 2025 at 7:49 PM
Our py-econometrics org has a new package: `maketables` is now installable from PyPi! You can create regression tables for PyFixest, statsmodels, linearmodels, and save them as html, latex, or word.
github: github.com/py-econometr...
docs: py-econometrics.github.io/maketables/
github: github.com/py-econometr...
docs: py-econometrics.github.io/maketables/
Very excited that #statsmodels (the standard #Python library for statistics) accepted a code contribution that makes it 💯 compatible with upcoming #DataMatrix 2.0 as well as other data formats! No more converting to and from pandas DataFrame. github.com/statsmodels/...
ENH: Support for array-like and pandas-like data by smathot · Pull Request #9582 · statsmodels/statsmodels
This is a small patch that does not change any functionality or fixes issues, but rather makes statsmodels more flexible in the types of data that it accepts. Specifically, right now statsmodels do...
github.com
July 9, 2025 at 11:21 AM
Very excited that #statsmodels (the standard #Python library for statistics) accepted a code contribution that makes it 💯 compatible with upcoming #DataMatrix 2.0 as well as other data formats! No more converting to and from pandas DataFrame. github.com/statsmodels/...
Where would Python/NumPy/pandas/statsmodels fit?
August 19, 2026 at 4:55 PM
Where would Python/NumPy/pandas/statsmodels fit?
Other open source trivia I learned implementing this - @vincentab.bsky.social implemented the quantreg implementation for statsmodels back in 2013(!): groups.google.com/g/pystatsmod...
R vs. Python+statsmodels
groups.google.com
May 25, 2025 at 1:30 PM
Other open source trivia I learned implementing this - @vincentab.bsky.social implemented the quantreg implementation for statsmodels back in 2013(!): groups.google.com/g/pystatsmod...
As it’s still a little under the radar - @vincentab.bsky.social & team have also developed a Python version of marginaleffects: github.com/vincentarelb... Works with statsmodels and pyfixest!
Our JSS article is out!
And now I get to focus on {marginaleffects} 1.0.0. Stay tuned.
www.jstatsoft.org/article/view...
And now I get to focus on {marginaleffects} 1.0.0. Stay tuned.
www.jstatsoft.org/article/view...
December 2, 2024 at 9:48 PM
As it’s still a little under the radar - @vincentab.bsky.social & team have also developed a Python version of marginaleffects: github.com/vincentarelb... Works with statsmodels and pyfixest!
My lovely partner's 1st book is coming out in a month or so. It's probably the driest read in the replies, but we're excited it's finally coming out.
January 10, 2025 at 9:32 PM
My lovely partner's 1st book is coming out in a month or so. It's probably the driest read in the replies, but we're excited it's finally coming out.
The schedule is up now for PyData Global 2024.
On Dec 5 at 13:00 UTC, I'll present a tutorial on Time Series Analysis with StatsModels:
global2024.pydata.org/cfp/talk/KLX...
On Dec 5 at 13:00 UTC, I'll present a tutorial on Time Series Analysis with StatsModels:
global2024.pydata.org/cfp/talk/KLX...
Time Series Analysis with StatsModels PyData Global 2024
Outline
* Introduction to time series
* Overview of the data
* Seasonal decomposition, additive model
* Seasonal decomposition, multiplicative model
* Serial correlation and autoregression
* ARIMA
* ...
global2024.pydata.org
November 18, 2024 at 4:48 PM
The schedule is up now for PyData Global 2024.
On Dec 5 at 13:00 UTC, I'll present a tutorial on Time Series Analysis with StatsModels:
global2024.pydata.org/cfp/talk/KLX...
On Dec 5 at 13:00 UTC, I'll present a tutorial on Time Series Analysis with StatsModels:
global2024.pydata.org/cfp/talk/KLX...
The schedule for PyCon US 2025 is live!
On May 15, I'm offering a new tutorial called "Analyzing Survey Data with Pandas and StatsModels". We'll explore data from the General Social Survey, run regression models, and visualize results.
us.pycon.org/2025/schedul...
On May 15, I'm offering a new tutorial called "Analyzing Survey Data with Pandas and StatsModels". We'll explore data from the General Social Survey, run regression models, and visualize results.
us.pycon.org/2025/schedul...
Analyzing Survey Data with Pandas and StatsModels
- PyCon US 2025
us.pycon.org
February 28, 2025 at 7:48 PM
The schedule for PyCon US 2025 is live!
On May 15, I'm offering a new tutorial called "Analyzing Survey Data with Pandas and StatsModels". We'll explore data from the General Social Survey, run regression models, and visualize results.
us.pycon.org/2025/schedul...
On May 15, I'm offering a new tutorial called "Analyzing Survey Data with Pandas and StatsModels". We'll explore data from the General Social Survey, run regression models, and visualize results.
us.pycon.org/2025/schedul...
statsmodels and pyfixest are good
January 9, 2025 at 11:35 AM
statsmodels and pyfixest are good
This workshop from the PyData Global 2024 conference by Allen Downey provides an introduction to time series analysis with the StatsModels Python library.
Definitely going to add it to my watch list!
www.youtube.com/watch?v=foMb...
#timeseries #forecasting #python #datascience
Definitely going to add it to my watch list!
www.youtube.com/watch?v=foMb...
#timeseries #forecasting #python #datascience
Allen Downey - Time Series Analysis with StatsModels | PyData Global 2024
YouTube video by PyData
www.youtube.com
April 21, 2025 at 1:20 PM
This workshop from the PyData Global 2024 conference by Allen Downey provides an introduction to time series analysis with the StatsModels Python library.
Definitely going to add it to my watch list!
www.youtube.com/watch?v=foMb...
#timeseries #forecasting #python #datascience
Definitely going to add it to my watch list!
www.youtube.com/watch?v=foMb...
#timeseries #forecasting #python #datascience
I'm presenting a workshop on Time Series Analysis at PyData Global, Thursday at 13:00 UTC
global2024.pydata.org/cfp/talk/KLX...
Good virtual seats still available!
global2024.pydata.org/cfp/talk/KLX...
Good virtual seats still available!
Time Series Analysis with StatsModels PyData Global 2024
Outline
* Introduction to time series
* Overview of the data
* Seasonal decomposition, additive model
* Seasonal decomposition, multiplicative model
* Serial correlation and autoregression
* ARIMA
* ...
global2024.pydata.org
December 4, 2024 at 7:49 PM
I'm presenting a workshop on Time Series Analysis at PyData Global, Thursday at 13:00 UTC
global2024.pydata.org/cfp/talk/KLX...
Good virtual seats still available!
global2024.pydata.org/cfp/talk/KLX...
Good virtual seats still available!
Model building - both statsmodels and sklearn - pretty smooth.
But that's where the smoothness ends it seems.
Trying to doing stuff with these models other than just making predictions is oh so very clunky.
(I don't know what people did overthere before #marginaleffects...)
But that's where the smoothness ends it seems.
Trying to doing stuff with these models other than just making predictions is oh so very clunky.
(I don't know what people did overthere before #marginaleffects...)
July 24, 2025 at 10:54 AM
Model building - both statsmodels and sklearn - pretty smooth.
But that's where the smoothness ends it seems.
Trying to doing stuff with these models other than just making predictions is oh so very clunky.
(I don't know what people did overthere before #marginaleffects...)
But that's where the smoothness ends it seems.
Trying to doing stuff with these models other than just making predictions is oh so very clunky.
(I don't know what people did overthere before #marginaleffects...)
btw doing I(rel_date**2) in a statsmodels patsy produces the same problem! Beware quadratics in your linear models in statsmodels!
May 13, 2025 at 9:36 PM
btw doing I(rel_date**2) in a statsmodels patsy produces the same problem! Beware quadratics in your linear models in statsmodels!
Would genuinely welcome this - I’ve used some of the count-based GLMs in statsmodels and it’s never felt totally great to use in notebooks
March 18, 2026 at 9:46 PM
Would genuinely welcome this - I’ve used some of the count-based GLMs in statsmodels and it’s never felt totally great to use in notebooks
The reason I ask, is that I nerdsniped myself into getting a flexible GLM implementation done in JAX alongside a grammar of inference.
Depends on how much community interest there is in a more general JAX-based regression library as a drop-in replacement for statsmodels, vs only GLM
Depends on how much community interest there is in a more general JAX-based regression library as a drop-in replacement for statsmodels, vs only GLM
Are folks using statsmodels from Python? If so, what are the primary features you use? Model fitting (which models)? Diagnostics (which diagnostics)?
March 18, 2026 at 9:42 PM
The reason I ask, is that I nerdsniped myself into getting a flexible GLM implementation done in JAX alongside a grammar of inference.
Depends on how much community interest there is in a more general JAX-based regression library as a drop-in replacement for statsmodels, vs only GLM
Depends on how much community interest there is in a more general JAX-based regression library as a drop-in replacement for statsmodels, vs only GLM
The cost behind the Python Quant Stack:
• SciPy: $0
• Zipline: $0
• Python: $0
• NumPy: $0
• PyFolio: $0
• Pandas : $0
• OpenBB: $0
• Empyrical: $0
• AlphaLens: $0
• Statsmodels: $0
• RiskFolio-Lib: $0
Anyone can get started.
• SciPy: $0
• Zipline: $0
• Python: $0
• NumPy: $0
• PyFolio: $0
• Pandas : $0
• OpenBB: $0
• Empyrical: $0
• AlphaLens: $0
• Statsmodels: $0
• RiskFolio-Lib: $0
Anyone can get started.
November 23, 2024 at 1:20 AM
The cost behind the Python Quant Stack:
• SciPy: $0
• Zipline: $0
• Python: $0
• NumPy: $0
• PyFolio: $0
• Pandas : $0
• OpenBB: $0
• Empyrical: $0
• AlphaLens: $0
• Statsmodels: $0
• RiskFolio-Lib: $0
Anyone can get started.
• SciPy: $0
• Zipline: $0
• Python: $0
• NumPy: $0
• PyFolio: $0
• Pandas : $0
• OpenBB: $0
• Empyrical: $0
• AlphaLens: $0
• Statsmodels: $0
• RiskFolio-Lib: $0
Anyone can get started.
the other commonly used logistic regression implementation in Python is statsmodels, which doesn’t do regularisation
August 7, 2025 at 10:49 AM
the other commonly used logistic regression implementation in Python is statsmodels, which doesn’t do regularisation
so we should just ignore the fact that it essentially copied github.com/statsmodels/...
github.com/statsmodels/... literally has comments that are exactly the same with slight word variations from the result of the "Vibe coding"
github.com/statsmodels/... literally has comments that are exactly the same with slight word variations from the result of the "Vibe coding"
statsmodels/statsmodels/nonparametric/bandwidths.py at main · statsmodels/statsmodels
Statsmodels: statistical modeling and econometrics in Python - statsmodels/statsmodels
github.com
January 5, 2026 at 8:22 AM
so we should just ignore the fact that it essentially copied github.com/statsmodels/...
github.com/statsmodels/... literally has comments that are exactly the same with slight word variations from the result of the "Vibe coding"
github.com/statsmodels/... literally has comments that are exactly the same with slight word variations from the result of the "Vibe coding"
I don't know this problem space well, but it was pretty easy to find slightly re-written code (including comments) from github.com/statsmodels/...
so was 3 hours and tokens burned cheaper and more productive than adding a well maintained dependency?
so was 3 hours and tokens burned cheaper and more productive than adding a well maintained dependency?
statsmodels/statsmodels/nonparametric/bandwidths.py at main · statsmodels/statsmodels
Statsmodels: statistical modeling and econometrics in Python - statsmodels/statsmodels
github.com
January 5, 2026 at 8:26 AM
I don't know this problem space well, but it was pretty easy to find slightly re-written code (including comments) from github.com/statsmodels/...
so was 3 hours and tokens burned cheaper and more productive than adding a well maintained dependency?
so was 3 hours and tokens burned cheaper and more productive than adding a well maintained dependency?
I've been using code to revisit math, mainly Python with Numpy, SymPy, Seaborn and statsmodels. I even taught myself calculus and linear algebra, though I think I'm leaning toward the latter. NumPy is numerical linear algebra already pretty much.
February 11, 2026 at 5:50 AM
I've been using code to revisit math, mainly Python with Numpy, SymPy, Seaborn and statsmodels. I even taught myself calculus and linear algebra, though I think I'm leaning toward the latter. NumPy is numerical linear algebra already pretty much.