Release 5.4.0

This is a compatibility release for pandas 2.0.

Release 5.3.1

This release fixes some small (non-code) issues with the 5.3.0 release documentation.

Release 5.3.0

This release contains two small fixes:

• Relax an overly specific assert that causes issues downstream
• Fix a typo in a literal type definition

Release 5.2.0

This is a bug fix release that fixes two small bugs.

Release 5.1.0

This is an enhancement that improves the DF-GLS test. It also adds official support for Python 3.10.

Release 5.0.1

This is a small release that fixes a packaging issue.

Release 5.0

Release 5.0 contains new features and backward-incompatible changes.

Unit Root

• All unit root tests are now immutable, and so properties such as trend cannot be set after the test is created.

Bootstrap

• Added seed keyword argument to all bootstraps (e.g., IIDBootstrap and StationaryBootstrap) that allows a NumPy numpy.random.Generator to be used. The seed keyword argument also accepts legacy numpy.random.RandomState instances and integers. If an integer is passed, the random number generator is constructed by calling numpy.random.default_rng The seed keyword argument replaces the random_state keyword argument.
• The IIDBootstrap.random_state property has also been deprecated in favor of IIDBootstrap.generator.
• The IIDBootstrap.get_state and IIDBootstrap.set_state methods have been replaced by the IIDBootstrap.state property.

Volatility Modeling

• Added seed keyword argument to all distributions (e.g., Normal and StudentsT) that allows a NumPy numpy.random.Generator to be used. The seed keyword argument also accepts legacy numpy.random.RandomState instances and integers. If an integer is passed, the random number generator is constructed by calling numpy.random.default_rng The seed keyword argument replaces the random_state keyword argument.
• The Normal.random_state property has also been deprecated in favor of Normal.generator.
• Added ARCHInMean mean process supporting (G)ARCH-in-mean models.
• Extended VolatilityProcess with VolatilityProcess.volatility_updaterthat contains a VolatilityUpdater to allow ARCHInMean to be created from different
  volatility processes.

Release 4.19

This is a feature and bug fix release. The two key new features are:

• The reduction in the size of the data returned when returning forecasts. This can lead to a reduction in memory allocation by factor of 1000x or more. To use the new feature, set reindex=True in forecast().
• Forecasting with exogenous variables is not possible.

Release 4.18

This release fixes two issues:

• Removes an accidental requirement on Python 3.7
• Improves performance when fitting models

Release 4.17

This is a bug-fix release that fixes a bug that affects the fitted conditional variance from EWMAVariance.