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This is a minor release with doc fixes and other small updates. The only notable feature is `PhillipsPerron.regression` which returns regression results from the model estimated as part of the test.
This is a feature and bug release. New Features Major * There are two major new features: long-run covariance estimation and cointegration analysis - Added Kernel-based long-run variance estimation in `arch.covariance.kernel`. Examples include the `arch.covariance.kernel.Bartlett` and the `arch.covariance.kernel.Parzen` kernels. All estimators support automatic bandwidth selection. - Added Engle-Granger (`arch.unitroot.cointegration.engle_granger`) and Phillips-Ouliaris `arch.unitroot.cointegration.phillips_ouliaris`) cointegration tests - Added three methods to estimate cointegrating vectors: `arch.unitroot.cointegration.CanonicalCointegratingReg`, `arch.unitroot.cointegration.DynamicOLS`, and `arch.unitroot.cointegration.FullyModifiedOLS`. Minor - Issue warnings when unit root tests are mutated. Will raise after 5.0 is released. - Improved exceptions in `arch.unitroot.ADF`, `arch.unitroot.KPSS`, `arch.unitroot.PhillipsPerron`, `arch.unitroot.VarianceRatio`, and `arch.unitroot.ZivotAndrews` when test specification is infeasible to the time series being too short or the required regression model having reduced rank. Bugs Fixed - Fixed a bug when using "bca" confidence intervals with ``extra_kwargs``. - Fixed a bug in `arch.univariate.SkewStudent` which did not use the user-provided `RandomState` when one was provided. This prevented reproducing simulated values.
- Restore the vendored copy of property_cached which is required to build conda packages
- Added typing support to all classes, functions, and methods. - Fixed an issue that caused tests to fail on SciPy 1.4+. - Dropped support for Python 3.5 inline with NEP 29. - Added methods to compute moment and lower partial moments for standardized residuals. See, for example, `SkewStudent.moment` and `SkewStudent.partial_moment`. - Fixed a bug that produced an OverflowError when a time series has no variance.
This is a feature and bug release. - Added `ARCHModelResult.std_resid` - Error in bootstraps if inputs are not ndarrays, DataFrames or Series. - 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.
This release contains 2 big fixes. - Fixed a bug in `arch_lm_test` that assumed that the model data is contained in a pandas Series. - Fixed a bug that can affect use in certain environments that reload modules.
This is a bug fix release: * Fix an import bug that prevents conda packages from being built
This is a feature and bug release. - Removed support for Python 2.7. - Added `auto_bandwidth` to compute optimized bandwidth for a number of common kernel covariance estimators. 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 `ARCHModelResult.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. - Added `ARCHModelResult.optimization_result` to simplify checking for convergence of the numerical optimizer. - Added `random_state` argument to `HARX.forecast` to allow a `RandomState` objects to be passed in when forecasting when `method='bootstrap'`. This allows the repeatable forecast to be produced. - Fixed a bug in `VarianceRatio` that used the wrong variance in nonrobust inference with overlapping samples.
This is a small release that fixes an issue identified after 4.8.0 was released where extension modules would not be correctly imported.
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.
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
This is a feature release with 1 new feature: * Add support for MIDAS volatility processes with Hyperbolic weighting
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
Packing only release to fix an issue on PyPi.
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
* Fix GED in `arch_model`
- 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 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
Minor release with bug fixes and the FixedVariance process. Adds support for 3.6 in anaconda.org.
Variance forecasting to ARCH models
Primarily doc fixes and a few bugs squished. No major new features.
Added a small number of features, primarily the `fix` method which allows models to be "fit" using user-specified parameters.
New release featuring many small fixes and three multiple comparison procedures: - Test of Superior Predictive Ability (Reality Check) - Model Confidence Set - Stepwise Multiple Comparison
Included code to perform unit root tests.
- Reorganized the ARCH code to allow expansion - Added a comprehensive bootstrap framework for future application in multiple comparison tests
Initial release of arch. Matches code available on pypi and binstar.