def stationarity_test_adf(
ts: TimeSeries,
maxlag: Union[None, int] = None,
regression: str = "c",
autolag: Union[None, str] = "AIC",
) -> set:
"""
Provides Augmented Dickey-Fuller unit root test for a time series,
using :func:`statsmodels.tsa.stattools.adfuller`. See [1]_.
Parameters
----------
ts
The time series to test.
maxlag
Maximum lag which is included in test, default value of 12*(nobs/100)^{1/4} is used when None.
regression
Constant and trend order to include in regression.
"c" : constant only (default).
"ct" : constant and trend.
"ctt" : constant, and linear and quadratic trend.
"n" : no constant, no trend.
autolag
Method to use when automatically determining the lag length among the values 0, 1, …, maxlag.
If "AIC" (default) or "BIC", then the number of lags is chosen to minimize the corresponding
information criterion. "t-stat" based choice of maxlag. Starts with maxlag and drops a lag
until the t-statistic on the last lag length is significant using a 5%-sized test.
If None, then the number of included lags is set to maxlag.
Returns
-------
set
| adf: The test statistic.
| pvalue: MacKinnon's approximate p-value based on [2]_.
| usedlag: The number of lags used.
| nobs: The number of observations used for the ADF regression and calculation of the critical values.
| critical: Critical values for the test statistic at the 1 %, 5 %, and 10 % levels. Based on [2]_.
| icbest: The maximized information criterion if autolag is not None.
References
----------
.. [1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.adfuller.html
.. [2] MacKinnon (1994, 2010)
"""
ts._assert_univariate()
ts._assert_deterministic()
return adfuller(ts.values(copy=False), maxlag, regression, autolag)
def stationarity_test_kpss(ts: TimeSeries,
regression: str = "c",
nlags: Union[str, int] = "auto") -> set:
"""
Provides Kwiatkowski-Phillips-Schmidt-Shin test for stationarity for a time series,
using :func:`statsmodels.tsa.stattools.kpss`. See [1]_.
Parameters
----------
ts
The time series to test.
regression
The null hypothesis for the KPSS test.
'c' : The data is stationary around a constant (default).
'ct' : The data is stationary around a trend.
nlags
Indicates the number of lags to be used. If 'auto' (default), lags is calculated using the data-dependent method
of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). If set to 'legacy',
uses int(12 * (n / 100)**(1 / 4)) , as outlined in Schwert (1989).
Returns
-------
set
| kpss_stat: The test statistic.
| pvalue: The p-value of the test. The p-value is interpolated from Table 1 in [2]_,
| and a boundary point is returned if the test statistic is outside the table of critical values,
| that is, if the p-value is outside the interval (0.01, 0.1).
| lags: The truncation lag parameter.
| crit: The critical values at 10%, 5%, 2.5% and 1%. Based on [2]_.
References
----------
.. [1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.kpss.html
.. [2] Kwiatkowski et al. (1992)
"""
ts._assert_univariate()
ts._assert_deterministic()
return kpss(ts.values(copy=False), regression, nlags)
def granger_causality_tests(
ts_cause: TimeSeries,
ts_effect: TimeSeries,
maxlag: int,
addconst: bool = True,
verbose: bool = True,
) -> None:
"""
Provides four tests for granger non causality of 2 time series using
:func:`statsmodels.tsa.stattools.grangercausalitytests`.
See [1]_.
Parameters
----------
ts_cause
A univariate deterministic time series. The statistical test determines if this time series
'Granger causes' the time series ts_effect (second parameter). Missing values are not supported.
if H_0 (non causality) is rejected (p near 0), then there is a 'granger causality'.
ts_effect
Univariate time series 'Granger caused' by ts_cause.
maxlag
If an integer, computes the test for all lags up to maxlag.
If an iterable, computes the tests only for the lags in maxlag.
addconst
Include a constant in the model.
verbose
Print results.
Returns
-------
Dict
All test results, dictionary keys are the number of lags. For each lag the values are a tuple,
with the first element a dictionary with test statistic, pvalues, degrees of freedom, the second element are
the OLS estimation results for the restricted model, the unrestricted model and the restriction (contrast)
matrix for the parameter f_test.
References
----------
.. [1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.grangercausalitytests.html
"""
ts_cause._assert_univariate()
ts_effect._assert_univariate()
ts_cause._assert_deterministic()
ts_effect._assert_deterministic()
raise_if_not(
ts_cause.freq == ts_effect.freq,
"ts_cause and ts_effect must have the same frequency.",
)
if not ts_cause.has_same_time_as(ts_effect):
logger.warning(
"ts_cause and ts_effect time series have different time index. "
"We will slice-intersect ts_cause with ts_effect.")
ts_cause = ts_cause.slice_intersect(ts_effect)
ts_effect = ts_effect.slice_intersect(ts_cause)
if not stationarity_tests(ts_cause):
logger.warning(
"ts_cause doesn't seem to be stationary. Please review granger causality validity in your problem context."
)
if not stationarity_tests(ts_effect):
logger.warning(
"ts_effect doesn't seem to be stationary. Please review granger causality validity in your problem context."
)
return grangercausalitytests(
np.concatenate(
(ts_effect.values(copy=False), ts_cause.values(copy=False)),
axis=1),
maxlag,
addconst,
verbose,
)