This is a minor release with doc fixes and other small updates. The only notable feature is
regression()which returns regression results from the model estimated as part of the test (GH395).
Improved exceptions in
ZivotAndrewswhen test specification is infeasible to the time series being too short or the required regression model having reduced rank (GH364).
Fixed a bug when using “bca” confidence intervals with
Issue warnings when unit root tests are mutated. Will raise after 5.0 is released.
Restored the vendored copy of
property_cachedfor conda package building.
Fixed an issue that caused tests to fail on SciPy 1.4+ (GH339).
Dropped support for Python 3.5 inline with NEP 29 (GH334).
Fixed a bug that produced an OverflowError when a time series has no variance (GH331).
Error if inputs are not ndarrays, DataFrames or Series (GH315).
Added a check that the covariance is non-zero when using “studentized” confidence intervals. If the function bootstrapped produces statistics with 0 variance, it is not possible to studentized (GH322).
Fixed a bug in arch_lm_test that assumed that the model data is contained in a pandas Series. (GH313).
Fixed a bug that can affect use in certain environments that reload modules (GH317).
Removed support for Python 2.7.
Added a parameter rescale to
arch_model()that allows the estimator to rescale data if it may help parameter estimation. If rescale=True, then the data will be rescaled by a power of 10 (e.g., 10, 100, or 1000) to produce a series with a residual variance between 1 and 1000. The model is then estimated on the rescaled data. The scale is reported
scale(). If rescale=None, a warning is produced if the data appear to be poorly scaled, but no change of scale is applied. If rescale=False, no scale change is applied and no warning is issued.
Fixed a bug when using the BCA bootstrap method where the leave-one-out jackknife used the wrong centering variable (GH288).
Fixed a bug which prevented extension modules from being correctly imported.
Added Zivot-Andrews unit root test
ZivotAndrews. This code was originally written by Jim Varanelli.
Added data dependent lag length selection to the KPSS test,
KPSS. This code was originally written by Jim Varanelli.
Added ability to set the
random_statewhen initializing a bootstrap (GH259).
Added support for Fractionally Integrated GARCH (FIGARCH) in
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 (GH248).
Added support for MIDAS volatility processes using Hyperbolic weighting in
Added a parameter to forecast that allows a user-provided callable random generator to be used in place of the model random generator (GH225).
Added a low memory automatic lag selection method that can be used with very large time-series.
Improved performance of automatic lag selection in ADF and related tests.
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.
FixedVariancevolatility 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
Fixed a bug that prevented
fixfrom being used with a new model (GH156).
Added ability to jointly estimate smoothing parameter in EWMA variance when fitting the model.
Added ability to pass optimization options to ARCH model estimation (GH195).
Added forecast code for mean forecasting
Added volatility hedgehog plot
fixto arch models which allows for user specified parameters instead of estimated parameters.
Added Hansen’s Skew T distribution to distribution (Stanislav Khrapov)
Updated IPython notebooks to latest IPython version
Bug and typo fixes to IPython notebooks
Changed MCS to give a pvalue of 1.0 to best model. Previously was NaN
last_obsfrom model initialization and to
fitmethod to simplify estimating a model over alternative samples (e.g., rolling window estimation)
hold_backto only accept integers so that is simply defined the number of observations held back. This number is now held out of the sample irrespective of the value of
Added multiple comparison procedures
Typographical and other small changes
Add unit root tests: * Augmented Dickey-Fuller * Dickey-Fuller GLS * Phillips-Perron * KPSS * Variance Ratio
Removed deprecated locations for ARCH modeling functions
Refactored to move the univariate routines to arch.univariate and added deprecation warnings in the old locations
Enable numba jit compilation in the python recursions
Added a bootstrap framework, which will be used in future versions. The bootstrap framework is general purpose and can be used via high-level functions such as conf_int or cov, or as a low level iterator using bootstrap