Source code for statsmodels.tsa.vector_ar.hypothesis_test_results

from statsmodels.iolib.table import SimpleTable
import numpy as np


[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 np.allclose(self.test_statistic, other.test_statistic) \ and np.allclose(self.crit_value, other.crit_value) \ and np.allclose(self.pvalue, other.pvalue) \ and np.allclose(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)