StationarityPhillipsPerron

class StationarityPhillipsPerron(lags=None, trend='c', test_type='tau', p_threshold=0.05)[source]

Test for unit root order 1 via the Phillips-Perron Unit Root Test.

Direct interface to PhillipsPerron test from the arch package.

Uses arch.unitroot.PhillipsPerron as a test for unit roots, and derives a boolean statement whether a series is stationary.

Also returns test results for the unit root test as fitted parameters.

Parameters:

lagsint, optional

The number of lags to use in the Newey-West estimator of the long-run covariance. If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)

trend{“n”, “c”, “ct”}, optional

The trend component to include in the test

“n” - No trend components
“c” - Include a constant (Default)
“ct” - Include a constant and linear time trend

test_type{“tau”, “rho”}

The test to use when computing the test statistic. “tau” is based on the t-stat and “rho” uses a test based on nobs times the re-centered regression coefficient

Attributes:

stationary_bool: whether the series in fit is integrated of order 1 more precisely, whether the null of the Phillips-Perron test is rejected at p_threshold
test_statistic_float: The PP test statistic, of running PhillipsPerron on y in fit
pvalue_floatfloat: p-value obtained when running PhillipsPerron on y in fit
usedlag_int: The number of lags used in the test.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.stationarity import StationarityPhillipsPerron
>>>
>>> X = load_airline()
>>> sty_est = StationarityPhillipsPerron()
>>> sty_est.fit(X)
StationarityPhillipsPerron(...)
>>> sty_est.get_fitted_params()["stationary"]
False

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit estimator and estimate parameters.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(X[, y])	Update fitted parameters on more data.