Release 4.8

This is a feature and bug release. Highlights include:

• Added Zivot-Andrews unit root test.
• Added data dependent lag length selection to the KPSS test.
• Added IndependentSamplesBootstrap to bootstrap inference on statistics from independent samples that may
  have uneven length.
• Added arch_lm_test to ARCH-LM tests on model residuals or standardized residuals.
• Fixed a bug in ADF when applying to very short time series.
• Added ability to set the random_state when initializing a bootstrap.

Release 4.7

This is a feature and bug release:

• Added support for Fractionally Integrated GARCH (FIGARCH)
• Enable user to specify a specific value of the backcast in place of the automatically generated value.
• Fixed a big where parameter-less models where incorrectly reported as having constant variance

Release 4.6.0

This is a feature release with 1 new feature:

• Add support for MIDAS volatility processes with Hyperbolic weighting

Release 4.5.0

This is a feature release with 1 new feature:

• Added a parameter to forecast that allows a user-provided callable random
  generator to be used in place of the model random generator

Release 4.4.1

Packing only release to fix an issue on PyPi.

Release 4.4

This is a minor release containing mostly bug fixes.

Changes include:

• Added named parameters to Dickey-Fuller regressions.
• Removed use of the module-level NumPy RandomState. All random number generators use separate RandomState instances.
• Fixed a bug that prevented 1-step forecasts with exogenous regressors
• Added the Generalized Error Distribution for univariate ARCH models
• Fixed a bug in MCS when using the max method that prevented all included models from being listed

Release 4.3.1

• Fix GED in arch_model

Release 4.3

• Fixed a bug that prevented 1-step forecasts with exogenous regressors
• Added the Generalized Error Distribution for univariate ARCH models
• Fixed a bug in MCS when using the max method that prevented all included models from being
  listed
• Added FixedVariance volatility process which allows pre-specified variances to be used with
  a mean model. This has been added to allow so-called zig-zag estimation where a mean model is
  estimated with a fixed variance, and then a variance model is estimated on the residuals using
  a ZeroMean variance process.

Release 4.2

Release containing all changes since 4.1 including:

• Fixed a bug that prevented fix from being used with a new model (:issue:156)
• Added first_obs and last_obs parameters to fix to mimic fit
• Added ability to jointly estimate smoothing parameter in EWMA variance when fitting the model

Release 4.1

Minor release with bug fixes and the FixedVariance process.
Adds support for 3.6 in anaconda.org.