Change Logs

Changes since 4.8.1

  • Removed support for Python 2.7.
  • Added auto_bandwidth() to compute optimized bandwidth for a number of common kernel covariance estimators (GH303). This code was written by Michael Rabba.
  • 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).
  • Added optimization_result() to simplify checking for convergence of the numerical optimizer (GH292).
  • Added random_state argument to forecast() to allow a RandomState object to be passed in when forecasting when method=’bootstrap’. This allows the repeatable forecast to be produced (GH290).
  • Fixed a bug in VarianceRatio that used the wrong variance in nonrobust inference with overlapping samples (GH286).

Release 4.8.1

  • Fixed a bug which prevented extension modules from being correctly imported.

Release 4.8

  • 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 IndependentSamplesBootstrap to perform bootstrap inference on statistics from independent samples that may have uneven length (GH260).
  • Added arch_lm_test() to perform ARCH-LM tests on model residuals or standardized residuals (GH261).
  • Fixed a bug in ADF when applying to very short time series (GH262).
  • Added ability to set the random_state when initializing a bootstrap (GH259).

Release 4.7

  • Added support for Fractionally Integrated GARCH (FIGARCH) in 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 (GH248).

Release 4.6

  • Added support for MIDAS volatility processes using Hyperbolic weighting in MidasHyperbolic (GH233).

Release 4.5

  • 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.

Release 4.4

  • 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

  • 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

  • Fixed a bug that prevented fix from being used with a new model (GH156).
  • 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.
  • Added ability to pass optimization options to ARCH model estimation (GH195).

Changes since 3.0

  • Added forecast code for mean forecasting
  • Added volatility hedgehog plot
  • Added fix to 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
  • Removed hold_back and last_obs from model initialization and to fit method to simplify estimating a model over alternative samples (e.g., rolling window estimation)
  • Redefined hold_back to 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 first_obs.

Changes since 2.1

  • Added multiple comparison procedures
  • Typographical and other small changes

Changes since 2.0

  • Add unit root tests: * Augmented Dickey-Fuller * Dickey-Fuller GLS * Phillips-Perron * KPSS * Variance Ratio
  • Removed deprecated locations for ARCH modeling functions

Changes since 1.0

  • 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