def __init__(self):
self.acf = self.results['acvarfft']
self.qstat = self.results['Q1']
self.res1 = acf(self.x, nlags=40, qstat=True, fft=True)
def acorr_ljungbox(x, lags=None, boxpierce=False):
'''Ljung-Box test for no autocorrelation
Parameters
----------
x : array_like, 1d
data series, regression residuals when used as diagnostic test
lags : None, int or array_like
If lags is an integer then this is taken to be the largest lag
that is included, the test result is reported for all smaller lag length.
If lags is a list or array, then all lags are included up to the largest
lag in the list, however only the tests for the lags in the list are
reported.
If lags is None, then the default maxlag is 12*(nobs/100)^{1/4}
boxpierce : {False, True}
If true, then additional to the results of the Ljung-Box test also the
Box-Pierce test results are returned
Returns
-------
lbvalue : float or array
test statistic
pvalue : float or array
p-value based on chi-square distribution
bpvalue : (optionsal), float or array
test statistic for Box-Pierce test
bppvalue : (optional), float or array
p-value based for Box-Pierce test on chi-square distribution
Notes
-----
Ljung-Box and Box-Pierce statistic differ in their scaling of the
autocorrelation function. Ljung-Box test is reported to have better
small sample properties.
could be extended to work with more than one series
1d or nd ? axis ? ravel ?
needs more testing
''Verification''
Looks correctly sized in Monte Carlo studies.
not yet compared to verified values
Examples
--------
see example script
References
----------
Greene
Wikipedia
'''
x = np.asarray(x)
nobs = x.shape[0]
if lags is None:
lags = range(1,41) #TODO: check default; SS: changed to 40
elif isinstance(lags, int):
lags = range(1,lags+1)
maxlag = max(lags)
lags = np.asarray(lags)
acfx = acf(x, nlags=maxlag) # normalize by nobs not (nobs-nlags)
# SS: unbiased=False is default now
# acf2norm = acfx[1:maxlag+1]**2 / (nobs - np.arange(1,maxlag+1))
acf2norm = acfx[:maxlag+1]**2 / (nobs - np.arange(1,maxlag+1))
qljungbox = nobs * (nobs+2) * np.cumsum(acf2norm)[lags-1]
pval = stats.chi2.sf(qljungbox, lags)
if not boxpierce:
return qljungbox, pval
else:
qboxpierce = nobs * np.cumsum(acfx[1:maxlag+1]**2)[lags]
pvalbp = stats.chi2.sf(qboxpierce, lags)
return qljungbox, pval, qboxpierce, pvalbp
def __init__(self):
self.acf = self.results['acvar']
#self.acf = np.concatenate(([1.], self.acf))
self.qstat = self.results['Q1']
self.res1 = acf(self.x, nlags=40, qstat=True)