"""
Statistical tools for time series analysis
"""
from statsmodels.compat.python import (iteritems, range, lrange, string_types, lzip,
zip, map)
import numpy as np
from numpy.linalg import LinAlgError
from scipy import stats
from statsmodels.regression.linear_model import OLS, yule_walker
from statsmodels.tools.tools import add_constant, Bunch
from .tsatools import lagmat, lagmat2ds, add_trend
from .adfvalues import mackinnonp, mackinnoncrit
from statsmodels.tsa.arima_model import ARMA
from statsmodels.compat.scipy import _next_regular
__all__ = ['acovf', 'acf', 'pacf', 'pacf_yw', 'pacf_ols', 'ccovf', 'ccf',
'periodogram', 'q_stat', 'coint', 'arma_order_select_ic',
'adfuller']
#NOTE: now in two places to avoid circular import
#TODO: I like the bunch pattern for this too.
class ResultsStore(object):
def __str__(self):
return self._str # pylint: disable=E1101
def _autolag(mod, endog, exog, startlag, maxlag, method, modargs=(),
fitargs=(), regresults=False):
"""
Returns the results for the lag length that maximimizes the info criterion.
Parameters
----------
mod : Model class
Model estimator class.
modargs : tuple
args to pass to model. See notes.
fitargs : tuple
args to pass to fit. See notes.
lagstart : int
The first zero-indexed column to hold a lag. See Notes.
maxlag : int
The highest lag order for lag length selection.
method : str {"aic","bic","t-stat"}
aic - Akaike Information Criterion
bic - Bayes Information Criterion
t-stat - Based on last lag
Returns
-------
icbest : float
Best information criteria.
bestlag : int
The lag length that maximizes the information criterion.
Notes
-----
Does estimation like mod(endog, exog[:,:i], *modargs).fit(*fitargs)
where i goes from lagstart to lagstart+maxlag+1. Therefore, lags are
assumed to be in contiguous columns from low to high lag length with
the highest lag in the last column.
"""
#TODO: can tcol be replaced by maxlag + 2?
#TODO: This could be changed to laggedRHS and exog keyword arguments if
# this will be more general.
results = {}
method = method.lower()
for lag in range(startlag, startlag + maxlag + 1):
mod_instance = mod(endog, exog[:, :lag], *modargs)
results[lag] = mod_instance.fit()
if method == "aic":
icbest, bestlag = min((v.aic, k) for k, v in iteritems(results))
elif method == "bic":
icbest, bestlag = min((v.bic, k) for k, v in iteritems(results))
elif method == "t-stat":
#stop = stats.norm.ppf(.95)
stop = 1.6448536269514722
for lag in range(startlag + maxlag, startlag - 1, -1):
icbest = np.abs(results[lag].tvalues[-1])
if np.abs(icbest) >= stop:
bestlag = lag
icbest = icbest
break
else:
raise ValueError("Information Criterion %s not understood.") % method
if not regresults:
return icbest, bestlag
else:
return icbest, bestlag, results
#this needs to be converted to a class like HetGoldfeldQuandt,
# 3 different returns are a mess
# See:
#Ng and Perron(2001), Lag length selection and the construction of unit root
#tests with good size and power, Econometrica, Vol 69 (6) pp 1519-1554
#TODO: include drift keyword, only valid with regression == "c"
# just changes the distribution of the test statistic to a t distribution
#TODO: autolag is untested
[docs]def adfuller(x, maxlag=None, regression="c", autolag='AIC',
store=False, regresults=False):
'''
Augmented Dickey-Fuller unit root test
The Augmented Dickey-Fuller test can be used to test for a unit root in a
univariate process in the presence of serial correlation.
Parameters
----------
x : array_like, 1d
data series
maxlag : int
Maximum lag which is included in test, default 12*(nobs/100)^{1/4}
regression : str {'c','ct','ctt','nc'}
Constant and trend order to include in regression
* 'c' : constant only (default)
* 'ct' : constant and trend
* 'ctt' : constant, and linear and quadratic trend
* 'nc' : no constant, no trend
autolag : {'AIC', 'BIC', 't-stat', None}
* if None, then maxlag lags are used
* if 'AIC' (default) or 'BIC', then the number of lags is chosen
to minimize the corresponding information criterium
* 't-stat' based choice of maxlag. Starts with maxlag and drops a
lag until the t-statistic on the last lag length is significant at
the 95 % level.
store : bool
If True, then a result instance is returned additionally to
the adf statistic (default is False)
regresults : bool
If True, the full regression results are returned (default is False)
Returns
-------
adf : float
Test statistic
pvalue : float
MacKinnon's approximate p-value based on MacKinnon (1994)
usedlag : int
Number of lags used.
nobs : int
Number of observations used for the ADF regression and calculation of
the critical values.
critical values : dict
Critical values for the test statistic at the 1 %, 5 %, and 10 %
levels. Based on MacKinnon (2010)
icbest : float
The maximized information criterion if autolag is not None.
regresults : RegressionResults instance
The
resstore : (optional) instance of ResultStore
an instance of a dummy class with results attached as attributes
Notes
-----
The null hypothesis of the Augmented Dickey-Fuller is that there is a unit
root, with the alternative that there is no unit root. If the pvalue is
above a critical size, then we cannot reject that there is a unit root.
The p-values are obtained through regression surface approximation from
MacKinnon 1994, but using the updated 2010 tables.
If the p-value is close to significant, then the critical values should be
used to judge whether to accept or reject the null.
The autolag option and maxlag for it are described in Greene.
Examples
--------
see example script
References
----------
Greene
Hamilton
P-Values (regression surface approximation)
MacKinnon, J.G. 1994. "Approximate asymptotic distribution functions for
unit-root and cointegration tests. `Journal of Business and Economic
Statistics` 12, 167-76.
Critical values
MacKinnon, J.G. 2010. "Critical Values for Cointegration Tests." Queen's
University, Dept of Economics, Working Papers. Available at
http://ideas.repec.org/p/qed/wpaper/1227.html
'''
if regresults:
store = True
trenddict = {None: 'nc', 0: 'c', 1: 'ct', 2: 'ctt'}
if regression is None or isinstance(regression, int):
regression = trenddict[regression]
regression = regression.lower()
if regression not in ['c', 'nc', 'ct', 'ctt']:
raise ValueError("regression option %s not understood") % regression
x = np.asarray(x)
nobs = x.shape[0]
if maxlag is None:
#from Greene referencing Schwert 1989
maxlag = int(np.ceil(12. * np.power(nobs / 100., 1 / 4.)))
xdiff = np.diff(x)
xdall = lagmat(xdiff[:, None], maxlag, trim='both', original='in')
nobs = xdall.shape[0] # pylint: disable=E1103
xdall[:, 0] = x[-nobs - 1:-1] # replace 0 xdiff with level of x
xdshort = xdiff[-nobs:]
if store:
resstore = ResultsStore()
if autolag:
if regression != 'nc':
fullRHS = add_trend(xdall, regression, prepend=True)
else:
fullRHS = xdall
startlag = fullRHS.shape[1] - xdall.shape[1] + 1 # 1 for level # pylint: disable=E1103
#search for lag length with smallest information criteria
#Note: use the same number of observations to have comparable IC
#aic and bic: smaller is better
if not regresults:
icbest, bestlag = _autolag(OLS, xdshort, fullRHS, startlag,
maxlag, autolag)
else:
icbest, bestlag, alres = _autolag(OLS, xdshort, fullRHS, startlag,
maxlag, autolag,
regresults=regresults)
resstore.autolag_results = alres
bestlag -= startlag # convert to lag not column index
#rerun ols with best autolag
xdall = lagmat(xdiff[:, None], bestlag, trim='both', original='in')
nobs = xdall.shape[0] # pylint: disable=E1103
xdall[:, 0] = x[-nobs - 1:-1] # replace 0 xdiff with level of x
xdshort = xdiff[-nobs:]
usedlag = bestlag
else:
usedlag = maxlag
icbest = None
if regression != 'nc':
resols = OLS(xdshort, add_trend(xdall[:, :usedlag + 1],
regression)).fit()
else:
resols = OLS(xdshort, xdall[:, :usedlag + 1]).fit()
adfstat = resols.tvalues[0]
# adfstat = (resols.params[0]-1.0)/resols.bse[0]
# the "asymptotically correct" z statistic is obtained as
# nobs/(1-np.sum(resols.params[1:-(trendorder+1)])) (resols.params[0] - 1)
# I think this is the statistic that is used for series that are integrated
# for orders higher than I(1), ie., not ADF but cointegration tests.
# Get approx p-value and critical values
pvalue = mackinnonp(adfstat, regression=regression, N=1)
critvalues = mackinnoncrit(N=1, regression=regression, nobs=nobs)
critvalues = {"1%" : critvalues[0], "5%" : critvalues[1],
"10%" : critvalues[2]}
if store:
resstore.resols = resols
resstore.maxlag = maxlag
resstore.usedlag = usedlag
resstore.adfstat = adfstat
resstore.critvalues = critvalues
resstore.nobs = nobs
resstore.H0 = ("The coefficient on the lagged level equals 1 - "
"unit root")
resstore.HA = "The coefficient on the lagged level < 1 - stationary"
resstore.icbest = icbest
return adfstat, pvalue, critvalues, resstore
else:
if not autolag:
return adfstat, pvalue, usedlag, nobs, critvalues
else:
return adfstat, pvalue, usedlag, nobs, critvalues, icbest
[docs]def acovf(x, unbiased=False, demean=True, fft=False):
'''
Autocovariance for 1D
Parameters
----------
x : array
Time series data. Must be 1d.
unbiased : bool
If True, then denominators is n-k, otherwise n
demean : bool
If True, then subtract the mean x from each element of x
fft : bool
If True, use FFT convolution. This method should be preferred
for long time series.
Returns
-------
acovf : array
autocovariance function
'''
x = np.squeeze(np.asarray(x))
if x.ndim > 1:
raise ValueError("x must be 1d. Got %d dims." % x.ndim)
n = len(x)
if demean:
xo = x - x.mean()
else:
xo = x
if unbiased:
xi = np.arange(1, n + 1)
d = np.hstack((xi, xi[:-1][::-1]))
else:
d = n * np.ones(2 * n - 1)
if fft:
nobs = len(xo)
Frf = np.fft.fft(xo, n=nobs * 2)
acov = np.fft.ifft(Frf * np.conjugate(Frf))[:nobs] / d[n - 1:]
return acov.real
else:
return (np.correlate(xo, xo, 'full') / d)[n - 1:]
[docs]def q_stat(x, nobs, type="ljungbox"):
"""
Return's Ljung-Box Q Statistic
x : array-like
Array of autocorrelation coefficients. Can be obtained from acf.
nobs : int
Number of observations in the entire sample (ie., not just the length
of the autocorrelation function results.
Returns
-------
q-stat : array
Ljung-Box Q-statistic for autocorrelation parameters
p-value : array
P-value of the Q statistic
Notes
------
Written to be used with acf.
"""
x = np.asarray(x)
if type == "ljungbox":
ret = (nobs * (nobs + 2) *
np.cumsum((1. / (nobs - np.arange(1, len(x) + 1))) * x**2))
chi2 = stats.chi2.sf(ret, np.arange(1, len(x) + 1))
return ret, chi2
#NOTE: Changed unbiased to False
#see for example
# http://www.itl.nist.gov/div898/handbook/eda/section3/autocopl.htm
[docs]def acf(x, unbiased=False, nlags=40, qstat=False, fft=False, alpha=None):
'''
Autocorrelation function for 1d arrays.
Parameters
----------
x : array
Time series data
unbiased : bool
If True, then denominators for autocovariance are n-k, otherwise n
nlags: int, optional
Number of lags to return autocorrelation for.
qstat : bool, optional
If True, returns the Ljung-Box q statistic for each autocorrelation
coefficient. See q_stat for more information.
fft : bool, optional
If True, computes the ACF via FFT.
alpha : scalar, optional
If a number is given, the confidence intervals for the given level are
returned. For instance if alpha=.05, 95 % confidence intervals are
returned where the standard deviation is computed according to
Bartlett\'s formula.
Returns
-------
acf : array
autocorrelation function
confint : array, optional
Confidence intervals for the ACF. Returned if confint is not None.
qstat : array, optional
The Ljung-Box Q-Statistic. Returned if q_stat is True.
pvalues : array, optional
The p-values associated with the Q-statistics. Returned if q_stat is
True.
Notes
-----
The acf at lag 0 (ie., 1) is returned.
This is based np.correlate which does full convolution. For very long time
series it is recommended to use fft convolution instead.
If unbiased is true, the denominator for the autocovariance is adjusted
but the autocorrelation is not an unbiased estimtor.
'''
nobs = len(x)
d = nobs # changes if unbiased
if not fft:
avf = acovf(x, unbiased=unbiased, demean=True)
#acf = np.take(avf/avf[0], range(1,nlags+1))
acf = avf[:nlags + 1] / avf[0]
else:
x = np.squeeze(np.asarray(x))
#JP: move to acovf
x0 = x - x.mean()
# ensure that we always use a power of 2 or 3 for zero-padding,
# this way we'll ensure O(n log n) runtime of the fft.
n = _next_regular(2 * nobs + 1)
Frf = np.fft.fft(x0, n=n) # zero-pad for separability
if unbiased:
d = nobs - np.arange(nobs)
acf = np.fft.ifft(Frf * np.conjugate(Frf))[:nobs] / d
acf /= acf[0]
#acf = np.take(np.real(acf), range(1,nlags+1))
acf = np.real(acf[:nlags + 1]) # keep lag 0
if not (qstat or alpha):
return acf
if alpha is not None:
varacf = np.ones(nlags + 1) / nobs
varacf[0] = 0
varacf[1] = 1. / nobs
varacf[2:] *= 1 + 2 * np.cumsum(acf[1:-1]**2)
interval = stats.norm.ppf(1 - alpha / 2.) * np.sqrt(varacf)
confint = np.array(lzip(acf - interval, acf + interval))
if not qstat:
return acf, confint
if qstat:
qstat, pvalue = q_stat(acf[1:], nobs=nobs) # drop lag 0
if alpha is not None:
return acf, confint, qstat, pvalue
else:
return acf, qstat, pvalue
[docs]def pacf_yw(x, nlags=40, method='unbiased'):
'''Partial autocorrelation estimated with non-recursive yule_walker
Parameters
----------
x : 1d array
observations of time series for which pacf is calculated
nlags : int
largest lag for which pacf is returned
method : 'unbiased' (default) or 'mle'
method for the autocovariance calculations in yule walker
Returns
-------
pacf : 1d array
partial autocorrelations, maxlag+1 elements
Notes
-----
This solves yule_walker for each desired lag and contains
currently duplicate calculations.
'''
pacf = [1.]
for k in range(1, nlags + 1):
pacf.append(yule_walker(x, k, method=method)[0][-1])
return np.array(pacf)
#NOTE: this is incorrect.
[docs]def pacf_ols(x, nlags=40):
'''Calculate partial autocorrelations
Parameters
----------
x : 1d array
observations of time series for which pacf is calculated
nlags : int
Number of lags for which pacf is returned. Lag 0 is not returned.
Returns
-------
pacf : 1d array
partial autocorrelations, maxlag+1 elements
Notes
-----
This solves a separate OLS estimation for each desired lag.
'''
#TODO: add warnings for Yule-Walker
#NOTE: demeaning and not using a constant gave incorrect answers?
#JP: demeaning should have a better estimate of the constant
#maybe we can compare small sample properties with a MonteCarlo
xlags, x0 = lagmat(x, nlags, original='sep')
#xlags = sm.add_constant(lagmat(x, nlags), prepend=True)
xlags = add_constant(xlags)
pacf = [1.]
for k in range(1, nlags+1):
res = OLS(x0[k:], xlags[k:, :k+1]).fit()
#np.take(xlags[k:], range(1,k+1)+[-1],
pacf.append(res.params[-1])
return np.array(pacf)
[docs]def pacf(x, nlags=40, method='ywunbiased', alpha=None):
'''Partial autocorrelation estimated
Parameters
----------
x : 1d array
observations of time series for which pacf is calculated
nlags : int
largest lag for which pacf is returned
method : 'ywunbiased' (default) or 'ywmle' or 'ols'
specifies which method for the calculations to use:
- yw or ywunbiased : yule walker with bias correction in denominator
for acovf
- ywm or ywmle : yule walker without bias correction
- ols - regression of time series on lags of it and on constant
- ld or ldunbiased : Levinson-Durbin recursion with bias correction
- ldb or ldbiased : Levinson-Durbin recursion without bias correction
alpha : scalar, optional
If a number is given, the confidence intervals for the given level are
returned. For instance if alpha=.05, 95 % confidence intervals are
returned where the standard deviation is computed according to
1/sqrt(len(x))
Returns
-------
pacf : 1d array
partial autocorrelations, nlags elements, including lag zero
confint : array, optional
Confidence intervals for the PACF. Returned if confint is not None.
Notes
-----
This solves yule_walker equations or ols for each desired lag
and contains currently duplicate calculations.
'''
if method == 'ols':
ret = pacf_ols(x, nlags=nlags)
elif method in ['yw', 'ywu', 'ywunbiased', 'yw_unbiased']:
ret = pacf_yw(x, nlags=nlags, method='unbiased')
elif method in ['ywm', 'ywmle', 'yw_mle']:
ret = pacf_yw(x, nlags=nlags, method='mle')
elif method in ['ld', 'ldu', 'ldunbiase', 'ld_unbiased']:
acv = acovf(x, unbiased=True)
ld_ = levinson_durbin(acv, nlags=nlags, isacov=True)
#print 'ld', ld_
ret = ld_[2]
# inconsistent naming with ywmle
elif method in ['ldb', 'ldbiased', 'ld_biased']:
acv = acovf(x, unbiased=False)
ld_ = levinson_durbin(acv, nlags=nlags, isacov=True)
ret = ld_[2]
else:
raise ValueError('method not available')
if alpha is not None:
varacf = 1. / len(x) # for all lags >=1
interval = stats.norm.ppf(1. - alpha / 2.) * np.sqrt(varacf)
confint = np.array(lzip(ret - interval, ret + interval))
confint[0] = ret[0] # fix confidence interval for lag 0 to varpacf=0
return ret, confint
else:
return ret
[docs]def ccovf(x, y, unbiased=True, demean=True):
''' crosscovariance for 1D
Parameters
----------
x, y : arrays
time series data
unbiased : boolean
if True, then denominators is n-k, otherwise n
Returns
-------
ccovf : array
autocovariance function
Notes
-----
This uses np.correlate which does full convolution. For very long time
series it is recommended to use fft convolution instead.
'''
n = len(x)
if demean:
xo = x - x.mean()
yo = y - y.mean()
else:
xo = x
yo = y
if unbiased:
xi = np.ones(n)
d = np.correlate(xi, xi, 'full')
else:
d = n
return (np.correlate(xo, yo, 'full') / d)[n - 1:]
[docs]def ccf(x, y, unbiased=True):
'''cross-correlation function for 1d
Parameters
----------
x, y : arrays
time series data
unbiased : boolean
if True, then denominators for autocovariance is n-k, otherwise n
Returns
-------
ccf : array
cross-correlation function of x and y
Notes
-----
This is based np.correlate which does full convolution. For very long time
series it is recommended to use fft convolution instead.
If unbiased is true, the denominator for the autocovariance is adjusted
but the autocorrelation is not an unbiased estimtor.
'''
cvf = ccovf(x, y, unbiased=unbiased, demean=True)
return cvf / (np.std(x) * np.std(y))
[docs]def periodogram(X):
"""
Returns the periodogram for the natural frequency of X
Parameters
----------
X : array-like
Array for which the periodogram is desired.
Returns
-------
pgram : array
1./len(X) * np.abs(np.fft.fft(X))**2
References
----------
Brockwell and Davis.
"""
X = np.asarray(X)
#if kernel == "bartlett":
# w = 1 - np.arange(M+1.)/M #JP removed integer division
pergr = 1. / len(X) * np.abs(np.fft.fft(X))**2
pergr[0] = 0. # what are the implications of this?
return pergr
#copied from nitime and statsmodels\sandbox\tsa\examples\try_ld_nitime.py
#TODO: check what to return, for testing and trying out returns everything
[docs]def levinson_durbin(s, nlags=10, isacov=False):
'''Levinson-Durbin recursion for autoregressive processes
Parameters
----------
s : array_like
If isacov is False, then this is the time series. If iasacov is true
then this is interpreted as autocovariance starting with lag 0
nlags : integer
largest lag to include in recursion or order of the autoregressive
process
isacov : boolean
flag to indicate whether the first argument, s, contains the
autocovariances or the data series.
Returns
-------
sigma_v : float
estimate of the error variance ?
arcoefs : ndarray
estimate of the autoregressive coefficients
pacf : ndarray
partial autocorrelation function
sigma : ndarray
entire sigma array from intermediate result, last value is sigma_v
phi : ndarray
entire phi array from intermediate result, last column contains
autoregressive coefficients for AR(nlags) with a leading 1
Notes
-----
This function returns currently all results, but maybe we drop sigma and
phi from the returns.
If this function is called with the time series (isacov=False), then the
sample autocovariance function is calculated with the default options
(biased, no fft).
'''
s = np.asarray(s)
order = nlags # rename compared to nitime
#from nitime
##if sxx is not None and type(sxx) == np.ndarray:
## sxx_m = sxx[:order+1]
##else:
## sxx_m = ut.autocov(s)[:order+1]
if isacov:
sxx_m = s
else:
sxx_m = acovf(s)[:order + 1] # not tested
phi = np.zeros((order + 1, order + 1), 'd')
sig = np.zeros(order + 1)
# initial points for the recursion
phi[1, 1] = sxx_m[1] / sxx_m[0]
sig[1] = sxx_m[0] - phi[1, 1] * sxx_m[1]
for k in range(2, order + 1):
phi[k, k] = (sxx_m[k] - np.dot(phi[1:k, k-1],
sxx_m[1:k][::-1])) / sig[k-1]
for j in range(1, k):
phi[j, k] = phi[j, k-1] - phi[k, k] * phi[k-j, k-1]
sig[k] = sig[k-1] * (1 - phi[k, k]**2)
sigma_v = sig[-1]
arcoefs = phi[1:, -1]
pacf_ = np.diag(phi).copy()
pacf_[0] = 1.
return sigma_v, arcoefs, pacf_, sig, phi # return everything
[docs]def grangercausalitytests(x, maxlag, addconst=True, verbose=True):
"""four tests for granger non causality of 2 timeseries
all four tests give similar results
`params_ftest` and `ssr_ftest` are equivalent based on F test which is
identical to lmtest:grangertest in R
Parameters
----------
x : array, 2d, (nobs,2)
data for test whether the time series in the second column Granger
causes the time series in the first column
maxlag : integer
the Granger causality test results are calculated for all lags up to
maxlag
verbose : bool
print results if true
Returns
-------
results : dictionary
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
teststatistic, 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.
Notes
-----
TODO: convert to class and attach results properly
The Null hypothesis for grangercausalitytests is that the time series in
the second column, x2, does NOT Granger cause the time series in the first
column, x1. Grange causality means that past values of x2 have a
statistically significant effect on the current value of x1, taking past
values of x1 into account as regressors. We reject the null hypothesis
that x2 does not Granger cause x1 if the pvalues are below a desired size
of the test.
The null hypothesis for all four test is that the coefficients
corresponding to past values of the second time series are zero.
'params_ftest', 'ssr_ftest' are based on F distribution
'ssr_chi2test', 'lrtest' are based on chi-square distribution
References
----------
http://en.wikipedia.org/wiki/Granger_causality
Greene: Econometric Analysis
"""
from scipy import stats
x = np.asarray(x)
if x.shape[0] <= 3 * maxlag + int(addconst):
raise ValueError("Insufficient observations. Maximum allowable "
"lag is {0}".format(int((x.shape[0] - int(addconst)) /
3) - 1))
resli = {}
for mlg in range(1, maxlag + 1):
result = {}
if verbose:
print('\nGranger Causality')
print('number of lags (no zero)', mlg)
mxlg = mlg
# create lagmat of both time series
dta = lagmat2ds(x, mxlg, trim='both', dropex=1)
#add constant
if addconst:
dtaown = add_constant(dta[:, 1:(mxlg + 1)], prepend=False)
dtajoint = add_constant(dta[:, 1:], prepend=False)
else:
raise NotImplementedError('Not Implemented')
#dtaown = dta[:, 1:mxlg]
#dtajoint = dta[:, 1:]
# Run ols on both models without and with lags of second variable
res2down = OLS(dta[:, 0], dtaown).fit()
res2djoint = OLS(dta[:, 0], dtajoint).fit()
#print results
#for ssr based tests see:
#http://support.sas.com/rnd/app/examples/ets/granger/index.htm
#the other tests are made-up
# Granger Causality test using ssr (F statistic)
fgc1 = ((res2down.ssr - res2djoint.ssr) /
res2djoint.ssr / mxlg * res2djoint.df_resid)
if verbose:
print('ssr based F test: F=%-8.4f, p=%-8.4f, df_denom=%d,'
' df_num=%d' % (fgc1,
stats.f.sf(fgc1, mxlg,
res2djoint.df_resid),
res2djoint.df_resid, mxlg))
result['ssr_ftest'] = (fgc1,
stats.f.sf(fgc1, mxlg, res2djoint.df_resid),
res2djoint.df_resid, mxlg)
# Granger Causality test using ssr (ch2 statistic)
fgc2 = res2down.nobs * (res2down.ssr - res2djoint.ssr) / res2djoint.ssr
if verbose:
print('ssr based chi2 test: chi2=%-8.4f, p=%-8.4f, '
'df=%d' % (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg))
result['ssr_chi2test'] = (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg)
#likelihood ratio test pvalue:
lr = -2 * (res2down.llf - res2djoint.llf)
if verbose:
print('likelihood ratio test: chi2=%-8.4f, p=%-8.4f, df=%d' %
(lr, stats.chi2.sf(lr, mxlg), mxlg))
result['lrtest'] = (lr, stats.chi2.sf(lr, mxlg), mxlg)
# F test that all lag coefficients of exog are zero
rconstr = np.column_stack((np.zeros((mxlg, mxlg)),
np.eye(mxlg, mxlg),
np.zeros((mxlg, 1))))
ftres = res2djoint.f_test(rconstr)
if verbose:
print('parameter F test: F=%-8.4f, p=%-8.4f, df_denom=%d,'
' df_num=%d' % (ftres.fvalue, ftres.pvalue, ftres.df_denom,
ftres.df_num))
result['params_ftest'] = (np.squeeze(ftres.fvalue)[()],
np.squeeze(ftres.pvalue)[()],
ftres.df_denom, ftres.df_num)
resli[mxlg] = (result, [res2down, res2djoint, rconstr])
return resli
[docs]def coint(y1, y2, regression="c"):
"""
This is a simple cointegration test. Uses unit-root test on residuals to
test for cointegrated relationship
See Hamilton (1994) 19.2
Parameters
----------
y1 : array_like, 1d
first element in cointegrating vector
y2 : array_like
remaining elements in cointegrating vector
c : str {'c'}
Included in regression
* 'c' : Constant
Returns
-------
coint_t : float
t-statistic of unit-root test on residuals
pvalue : float
MacKinnon's approximate p-value based on MacKinnon (1994)
crit_value : dict
Critical values for the test statistic at the 1 %, 5 %, and 10 %
levels.
Notes
-----
The Null hypothesis is that there is no cointegration, the alternative
hypothesis is that there is cointegrating relationship. If the pvalue is
small, below a critical size, then we can reject the hypothesis that there
is no cointegrating relationship.
P-values are obtained through regression surface approximation from
MacKinnon 1994.
References
----------
MacKinnon, J.G. 1994. "Approximate asymptotic distribution functions for
unit-root and cointegration tests. `Journal of Business and Economic
Statistics` 12, 167-76.
"""
regression = regression.lower()
if regression not in ['c', 'nc', 'ct', 'ctt']:
raise ValueError("regression option %s not understood") % regression
y1 = np.asarray(y1)
y2 = np.asarray(y2)
if regression == 'c':
y2 = add_constant(y2, prepend=False)
st1_resid = OLS(y1, y2).fit().resid # stage one residuals
lgresid_cons = add_constant(st1_resid[0:-1], prepend=False)
uroot_reg = OLS(st1_resid[1:], lgresid_cons).fit()
coint_t = (uroot_reg.params[0] - 1) / uroot_reg.bse[0]
pvalue = mackinnonp(coint_t, regression="c", N=2, lags=None)
crit_value = mackinnoncrit(N=1, regression="c", nobs=len(y1))
return coint_t, pvalue, crit_value
def _safe_arma_fit(y, order, model_kw, trend, fit_kw, start_params=None):
try:
return ARMA(y, order=order, **model_kw).fit(disp=0, trend=trend,
start_params=start_params,
**fit_kw)
except LinAlgError:
# SVD convergence failure on badly misspecified models
return
except ValueError as error:
if start_params is not None: # don't recurse again
# user supplied start_params only get one chance
return
# try a little harder, should be handled in fit really
elif ((hasattr(error, 'message') and 'initial' not in error.message)
or 'initial' in str(error)): # py2 and py3
start_params = [.1] * sum(order)
if trend == 'c':
start_params = [.1] + start_params
return _safe_arma_fit(y, order, model_kw, trend, fit_kw,
start_params)
else:
return
except: # no idea what happened
return
[docs]def arma_order_select_ic(y, max_ar=4, max_ma=2, ic='bic', trend='c',
model_kw={}, fit_kw={}):
"""
Returns information criteria for many ARMA models
Parameters
----------
y : array-like
Time-series data
max_ar : int
Maximum number of AR lags to use. Default 4.
max_ma : int
Maximum number of MA lags to use. Default 2.
ic : str, list
Information criteria to report. Either a single string or a list
of different criteria is possible.
trend : str
The trend to use when fitting the ARMA models.
model_kw : dict
Keyword arguments to be passed to the ``ARMA`` model
fit_kw : dict
Keyword arguments to be passed to ``ARMA.fit``.
Returns
-------
obj : Results object
Each ic is an attribute with a DataFrame for the results. The AR order
used is the row index. The ma order used is the column index. The
minimum orders are available as ``ic_min_order``.
Examples
--------
>>> from statsmodels.tsa.arima_process import arma_generate_sample
>>> import statsmodels.api as sm
>>> import numpy as np
>>> arparams = np.array([.75, -.25])
>>> maparams = np.array([.65, .35])
>>> arparams = np.r_[1, -arparams]
>>> maparam = np.r_[1, maparams]
>>> nobs = 250
>>> np.random.seed(2014)
>>> y = arma_generate_sample(arparams, maparams, nobs)
>>> res = sm.tsa.arma_order_select_ic(y, ic=['aic', 'bic'], trend='nc')
>>> res.aic_min_order
>>> res.bic_min_order
Notes
-----
This method can be used to tentatively identify the order of an ARMA
process, provided that the time series is stationary and invertible. This
function computes the full exact MLE estimate of each model and can be,
therefore a little slow. An implementation using approximate estimates
will be provided in the future. In the meantime, consider passing
{method : 'css'} to fit_kw.
"""
from pandas import DataFrame
ar_range = lrange(0, max_ar + 1)
ma_range = lrange(0, max_ma + 1)
if isinstance(ic, string_types):
ic = [ic]
elif not isinstance(ic, (list, tuple)):
raise ValueError("Need a list or a tuple for ic if not a string.")
results = np.zeros((len(ic), max_ar + 1, max_ma + 1))
for ar in ar_range:
for ma in ma_range:
if ar == 0 and ma == 0 and trend == 'nc':
results[:, ar, ma] = np.nan
continue
mod = _safe_arma_fit(y, (ar, ma), model_kw, trend, fit_kw)
if mod is None:
results[:, ar, ma] = np.nan
continue
for i, criteria in enumerate(ic):
results[i, ar, ma] = getattr(mod, criteria)
dfs = [DataFrame(res, columns=ma_range, index=ar_range) for res in results]
res = dict(zip(ic, dfs))
# add the minimums to the results dict
min_res = {}
for i, result in iteritems(res):
mins = np.where(result.min().min() == result)
min_res.update({i + '_min_order' : (mins[0][0], mins[1][0])})
res.update(min_res)
return Bunch(**res)
if __name__ == "__main__":
import statsmodels.api as sm
data = sm.datasets.macrodata.load().data
x = data['realgdp']
# adf is tested now.
adf = adfuller(x, 4, autolag=None)
adfbic = adfuller(x, autolag="bic")
adfaic = adfuller(x, autolag="aic")
adftstat = adfuller(x, autolag="t-stat")
# acf is tested now
acf1, ci1, Q, pvalue = acf(x, nlags=40, confint=95, qstat=True)
acf2, ci2, Q2, pvalue2 = acf(x, nlags=40, confint=95, fft=True, qstat=True)
acf3, ci3, Q3, pvalue3 = acf(x, nlags=40, confint=95, qstat=True,
unbiased=True)
acf4, ci4, Q4, pvalue4 = acf(x, nlags=40, confint=95, fft=True, qstat=True,
unbiased=True)
# pacf is tested now
# pacf1 = pacorr(x)
# pacfols = pacf_ols(x, nlags=40)
# pacfyw = pacf_yw(x, nlags=40, method="mle")
y = np.random.normal(size=(100, 2))
grangercausalitytests(y, 2)