statsmodels

This is a bug fix and future-proofing release that contains all bug fixes that have been applied since 0.14.0 was released.

There are no enhancements or changes to the statsmdoels API.

The statsmodels developers are happy to announce the first release of the 0.14 branch. 255 issues were closed in this release and 345 pull requests were merged. Major new features include:

• Treatment effect estimation and inference
• Hurdle and Truncated Count Regression
• Archimedean copulas have been extended to more than 2-dimensions.
• Multiseason STL decomposition (MSTL)
• Improved compatibility with current and future NumPy, SciPy, and pandas.

The statsmodels developers are happy to announce the first release candidate for 0.14.0. 248 issues were closed in this release and 335 pull requests were merged. Major new features include:

• Treatment effect estimation and inference
• Hurdle and Truncated Count Regression
• Archimedean copulas have been extended to more than 2-dimensions
• Multiseason STL decomposition (MSTL)
• Improved compatibility with current and future NumPy, SciPy and pandas

The statsmodels developers are happy to announce the Python 3.11 compatibility release for the 0.13 branch.

This release contains no bug fixes other than any needed to ensure statsmodels is compatible with Python 3.11. It also resolves an issue with PyPI that affects 0.13.4.

The statsmodels developers are happy to announce the Python 3.11 compatibility release for the 0.13 branch. This release contains no bug fixes other than any needed to ensure statsmodels is compatible with Python 3.11. It also resolves an issue with the source code generation in 0.13.3 that affects installs on Python 3.11 that use the source tarball.

The statsmodels developers are happy to announce the Python 3.11 compatibility release for the 0.13 branch. This release contains no bug fixes other than any needed to ensure statsmodels is compatible with Python 3.11.

The statsmodels developers are happy to announce the bugfix release for the 0.13 branch. This release fixes 10 bugs and provides protection against changes in recent versions of upstream packages.

The statsmodels developers are happy to announce the bug fix release for the 0.13 branch. This release fixes 8 bugs and brings initial support for Python 3.10.

The statsmodels developers are happy to announce the first release candidate for 0.13.0. 227 issues were closed in this release and 143 pull requests were merged. Major new features include:

• Autoregressive Distributed Lag models
• Copulas
• Ordered Models (Ordinal Regression)
• Beta Regression
• Improvements to ARIMA estimation options

The statsmodels developers are happy to announce the first release candidate for 0.13.0. 227 issues were closed in this release and 143 pull requests were merged. Major new features include:

• Autoregressive Distributed Lag models
• Copulas
• Ordered Models (Ordinal Regression)
• Beta Regression
• Improvements to ARIMA estimation options

This is a bug-fix release from the 0.12.x branch. Users are encouraged to upgrade.

Notable changes include fixes for a bug that could lead to incorrect results in forecasts with the new ARIMA model (when d > 0 and trend='t') and a bug in the LM test for autocorrelation.

This is a minor release from the 0.12.x branch with bug fixes and essential maintenance only.

The statsmodels developers are happy to announce release 0.12.0. 239 issues were closed in this release and 221 pull requests were merged.

Major new features include:

• New exponential smoothing model: ETS (Error, Trend, Seasonal)
• New dynamic factor model for large datasets and monthly/quarterly mixed frequency models
• Decomposition of forecast updates based on the "news"
• Sparse Cholesky Simulation Smoother
• Option to use Chandrasekhar recursions
• Two popular methods for forecasting time series, forecasting after STL decomposition and the Theta model
• Functions for constructing complex Deterministic Terms in time series models
• New statistics function: one-way ANOVA-type tests, hypothesis tests for 2-samples and meta-analysis.

The statsmodels developers are happy to announce the first release candidate for 0.12.0. 223 issues were closed in this release and 208 pull requests were merged. Major new features include:

• New exponential smoothing model: ETS (Error, Trend, Seasonal)
• New dynamic factor model for large datasets and monthly/quarterly mixed frequency models
• Decomposition of forecast updates based on the "news"
• Sparse Cholesky Simulation Smoother
• Option to use Chandrasekhar recursions
• Two popular methods for forecasting time series, forecasting after STL decomposition and the Theta model
• Functions for constructing complex Deterministic Terms in time series models

This is a bug fix release. It fixes a small number of bugs including two that affect the installation on statmodels on Python 2.7 and 3.8.

See the full release notes (or in rst format) for the full set of backported pull requests.

statsmodels developers are happy to announce a new release.

Major new features include:

• Regression
  • Rolling OLS and WLS
• Statistics
  • Oaxaca-Blinder decomposition
  • Distance covariance measures (new in RC2)
  • New regression diagnostic tools (new in RC2)
• Statespace Models
  • Statespace-based Linear exponential smoothing models¶
  • Methods to apply parameters fitted on one dataset to another dataset¶
  • Method to hold some parameters fixed at known values
  • Option for low memory operations
  • Improved access to state estimates
  • Improved simulation and impulse responses for time-varying models
• Time-Series Analysis
  • STL Decomposition
  • New AR model
  • New ARIMA model
  • Zivot-Andrews Test
  • More robust regime-switching models

See release notes for full details.

The second and final release candidate for statsmodels 0.11.

Major new features include:

• Regression
  • Rolling OLS and WLS
• Statistics
  • Oaxaca-Blinder decomposition
  • Distance covariance measures (new in RC2)
  • New regression diagnostic tools (new in RC2)
• Statespace Models
  • Statespace-based Linear exponential smoothing models¶
  • Methods to apply parameters fitted on one dataset to another dataset¶
  • Method to hold some parameters fixed at known values
  • Option for low memory operations
  • Improved access to state estimates
  • Improved simulation and impulse responses for time-varying models
• Time-Series Analysis
  • STL Decomposition
  • New AR model
  • New ARIMA model
  • Zivot-Andrews Test
  • More robust regime-switching models

See release notes for full details.

Release candidate for statsmodels 0.11.

Major new features include:

• Regression
  • Rolling OLS and WLS
• Statistics
  • Oaxaca-Blinder decomposition
• Statespace Models
  • Statespace-based Linear exponential smoothing models¶
  • Methods to apply parameters fitted on one dataset to another dataset¶
  • Method to hold some parameters fixed at known values
  • Option for low memory operations
  • Improved access to state estimates
  • Improved simulation and impulse responses for time-varying models
• Time-Series Analysis
  • STL Decomposition
  • New AR model
  • New ARIMA model
  • Zivot-Andrews Test
  • More robust regime switching models

See release notes for full details.

This is a minor release from the 0.10.x branch with bug fixes and essential maintenance only. The key new feature is:

• Compatibility with Python 3.8

This is a minor release from the 0.10.x branch with bug fixes and essential maintenance only. The key features are:

• Compatibility with pandas 0.25
• Compatibility with Numpy 1.17