Version 4¶

Release 4.19¶

Added the keyword argument reindex to forecast() that allows the returned forecasts to have minimal size when reindex=False. The default is reindex=True which preserved the current behavior. This will change in a future release. Using reindex=True often requires substantially more memory than when reindex=False. This is especially true when using simulation or bootstrap-based forecasting.
The default value reindex can be changed by importing

from arch.__future__ import reindexing

Fixed handling of exogenous regressors in forecast(). It is now possible to pass values for \(E_t[X_{t+h}]\) using the x argument.

Improved fit() performance of ARCH models.
Fixed a bug where `typing_extensions was subtly introduced as a run-time dependency.

Fixed a bug that produced incorrect conditional volatility from EWMA models (GH458).

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).

Added Kernel-based long-run variance estimation in arch.covariance.kernel. Examples include the Bartlett and the Parzen kernels. All estimators suppose automatic bandwidth selection.
Improved exceptions in ADF, KPSS, PhillipsPerron, VarianceRatio, and ZivotAndrews when 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 extra_kwargs (GH366).
Added Phillips-Ouliaris (phillips_ouliaris()) cointegration tests (GH360).
Added three methods to estimate cointegrating vectors: CanonicalCointegratingReg, DynamicOLS, and FullyModifiedOLS (GH356, GH359).
Added the Engle-Granger (engle_granger()) cointegration test (GH354).
Issue warnings when unit root tests are mutated. Will raise after 5.0 is released.
Fixed a bug in arch.univariate.SkewStudent which did not use the user-provided RandomState when one was provided. This prevented reproducing simulated values (GH353).

Added typing support to all classes, functions and methods (GH338, GH341, GH342, GH343, GH345, GH346).
Fixed an issue that caused tests to fail on SciPy 1.4+ (GH339).
Dropped support for Python 3.5 inline with NEP 29 (GH334).
Added methods to compute moment and lower partial moments for standardized residuals. See, for example, moment() and partial_moment() (GH329).
Fixed a bug that produced an OverflowError when a time series has no variance (GH331).

Added std_resid() (GH326).
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 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).

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).

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).

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

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.

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.

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).