from statsmodels.iolib.table import SimpleTable
[docs]class HypothesisTestResults(object):
"""
Results class for hypothesis tests.
Parameters
----------
test_statistic : float
crit_value : float
pvalue : float, 0 <= `pvalue` <= 1
df : int
Degrees of freedom.
signif : float, 0 < `signif` < 1
Significance level.
method : str
The kind of test (e.g. ``"f"`` for F-test, ``"wald"`` for Wald-test).
title : str
A title describing the test. It will be part of the summary.
h0 : str
A string describing the null hypothesis. It will be used in the
summary.
"""
def __init__(self, test_statistic, crit_value, pvalue, df,
signif, method, title, h0, ):
self.test_statistic = test_statistic
self.crit_value = crit_value
self.pvalue = pvalue
self.df = df
self.signif = signif
self.method = method.capitalize()
if test_statistic < crit_value:
self.conclusion = "fail to reject"
else:
self.conclusion = "reject"
self.title = title
self.h0 = h0
self.conclusion_str = "Conclusion: %s H_0" % self.conclusion
self.signif_str = " at {:.0%} significance level".format(self.signif)
[docs] def summary(self):
title = self.title + ". " + self.h0 + ". " \
+ self.conclusion_str + self.signif_str + "."
data_fmt = {"data_fmts": ["%#0.4g", "%#0.4g", "%#0.3F", "%s"]}
html_data_fmt = dict(data_fmt)
html_data_fmt["data_fmts"] = ["<td>" + i + "</td>"
for i in html_data_fmt["data_fmts"]]
return SimpleTable(data=[[self.test_statistic, self.crit_value,
self.pvalue, str(self.df)]],
headers=['Test statistic', 'Critical value',
'p-value', 'df'],
title=title,
txt_fmt=data_fmt,
html_fmt=html_data_fmt,
ltx_fmt=data_fmt)
def __str__(self):
return "<" + self.__module__ + "." + self.__class__.__name__ \
+ " object. " + self.h0 + ": " + self.conclusion \
+ self.signif_str \
+ ". Test statistic: {:.3f}".format(self.test_statistic) \
+ ", critical value: {:.3f}>".format(self.crit_value) \
+ ", p-value: {:.3f}>".format(self.pvalue)
def __eq__(self, other):
if not isinstance(other, self.__class__):
return False
return (self.test_statistic == other.test_statistic) \
and (self.crit_value == other.crit_value) \
and (self.pvalue == other.pvalue) \
and (self.signif == other.signif)
[docs]class CausalityTestResults(HypothesisTestResults):
"""
Results class for Granger-causality and instantaneous causality.
Parameters
----------
causing : list of str
This list contains the potentially causing variables.
caused : list of str
This list contains the potentially caused variables.
test_statistic : float
crit_value : float
pvalue : float
df : int
Degrees of freedom.
signif : float
Significance level.
test : str {``"granger"``, ``"inst"``}, default: ``"granger"``
If ``"granger"``, Granger-causality has been tested. If ``"inst"``,
instantaneous causality has been tested.
method : str {``"f"``, ``"wald"``}
The kind of test. ``"f"`` indicates an F-test, ``"wald"`` indicates a
Wald-test.
"""
def __init__(self, causing, caused, test_statistic, crit_value, pvalue, df,
signif, test="granger", method=None):
self.causing = causing
self.caused = caused
self.test = test
if method is None or method.lower() not in ["f", "wald"]:
raise ValueError('The method ("f" for F-test, "wald" for '
"Wald-test) must not be None.")
method = method.capitalize()
# attributes used in summary and string representation:
title = "Granger" if self.test == "granger" else "Instantaneous"
title += " causality %s-test" % method
h0 = "H_0: "
if len(self.causing) == 1:
h0 += "{} does not ".format(self.causing[0])
else:
h0 += "%s do not ".format(self.causing)
h0 += "Granger-" if self.test == "granger" else "instantaneously "
h0 += "cause "
if len(self.caused) == 1:
h0 += self.caused[0]
else:
h0 += "[" + ", ".join(caused) + "]"
super(CausalityTestResults, self).__init__(test_statistic, crit_value,
pvalue, df, signif, method,
title, h0)
def __eq__(self, other):
basic_test = super(CausalityTestResults, self).__eq__(other)
if not basic_test:
return False
test = self.test == other.test
variables = self.causing == other.causing and \
self.caused == other.caused
# instantaneous causality is a symmetric relation ==> causing and
# caused may be swapped
if not variables and self.test == "inst":
variables = self.causing == other.caused and \
self.caused == other.causing
return test and variables
[docs]class NormalityTestResults(HypothesisTestResults):
"""
Results class for the Jarque-Bera-test for nonnormality.
Parameters
----------
test_statistic : float
The test's test statistic.
crit_value : float
The test's critical value.
pvalue : float
The test's p-value.
df : int
Degrees of freedom.
signif : float
Significance level.
"""
def __init__(self, test_statistic, crit_value, pvalue, df, signif):
method = "Jarque-Bera"
title = "normality (skew and kurtosis) test"
h0 = 'H_0: data generated by normally-distributed process'
super(NormalityTestResults, self).__init__(test_statistic, crit_value,
pvalue, df, signif,
method, title, h0)
[docs]class WhitenessTestResults(HypothesisTestResults):
"""
Results class for the Portmanteau-test for residual autocorrelation.
Parameters
----------
test_statistic : float
The test's test statistic.
crit_value : float
The test's critical value.
pvalue : float
The test's p-value.
df : int
Degrees of freedom.
signif : float
Significance level.
nlags : int
Number of lags tested.
"""
def __init__(self, test_statistic, crit_value, pvalue, df, signif, nlags,
adjusted):
self.lags = nlags
self.adjusted = adjusted
method = "Portmanteau"
title = "{}-test for residual autocorrelation".format(method)
if adjusted:
title = "Adjusted " + title
h0 = "H_0: residual autocorrelation up to lag {} is zero".format(nlags)
super(WhitenessTestResults, self).__init__(test_statistic, crit_value,
pvalue, df, signif,
method, title, h0)
