def _top_right(self) -> List[Tuple[str, str]]:
top_right = [
("No. Observations:", str(self._resid.shape[0])),
("R²:", str_format(self.rsquared)),
("Adjusted. R²:", str_format(self.rsquared_adj)),
("Residual Variance:", str_format(self.residual_variance)),
("Long-run Variance:", str_format(self.long_run_variance)),
("", ""),
]
return top_right
def summary(self, full: bool = False) -> Summary:
"""
Summary of the model, containing estimated parameters and std. errors
Parameters
----------
full : bool, default False
Flag indicating whether to include all estimated parameters
(True) or only the parameters of the cointegrating vector
Returns
-------
Summary
A summary instance with method that support export to text, csv
or latex.
"""
if self._bandwidth != int(self._bandwidth):
bw = str_format(self._bandwidth)
else:
bw = str(int(self._bandwidth))
top_left = [
("Trend:", SHORT_TREND_DESCRIPTION[self._trend]),
("Leads:", str(self._leads)),
("Lags:", str(self._lags)),
("Cov Type:", str(self._cov_type)),
("Kernel:", str(self._kernel)),
("Bandwidth:", bw),
]
top_right = self._top_right()
smry = Summary()
typ = "Cointegrating Vector" if not full else "Model"
title = f"Dynamic OLS {typ} Summary"
table = self._top_table(top_left, top_right, title)
# Top Table
# Parameter table
smry.tables.append(table)
if full:
params = np.asarray(self.full_params)
stubs = list(self.full_params.index)
se = np.sqrt(np.diag(self.full_cov))
tstats = params / se
pvalues = 2 * (1 - stats.norm.cdf(np.abs(tstats)))
else:
params = np.asarray(self.params)
stubs = list(self.params.index)
se = np.asarray(self.std_errors)
tstats = np.asarray(self.tvalues)
pvalues = np.asarray(self.pvalues)
title = "Cointegrating Vector" if not full else "Model Parameters"
assert isinstance(se, np.ndarray)
table = self._param_table(params, se, tstats, pvalues, stubs, title)
smry.tables.append(table)
return smry
def summary(self) -> Summary:
"""
Summary of the model, containing estimated parameters and std. errors
Returns
-------
Summary
A summary instance with method that support export to text, csv
or latex.
"""
if self._bandwidth != int(self._bandwidth):
bw = str_format(self._bandwidth)
else:
bw = str(int(self._bandwidth))
top_left = [
("Trend:", SHORT_TREND_DESCRIPTION[self._trend]),
("Kernel:", str(self._kernel)),
("Bandwidth:", bw),
("", ""),
("", ""),
("", ""),
]
top_right = self._top_right()
smry = Summary()
title = self._estimator_type
table = self._top_table(top_left, top_right, title)
# Top Table
# Parameter table
smry.tables.append(table)
params = np.asarray(self.params)
stubs = list(self.params.index)
se = np.asarray(self.std_errors)
tstats = np.asarray(self.tvalues)
pvalues = np.asarray(self.pvalues)
title = "Cointegrating Vector"
table = self._param_table(params, se, tstats, pvalues, stubs, title)
smry.tables.append(table)
return smry
def __init__(
self,
stat: float,
pvalue: float,
crit_vals: pd.Series,
null: str = "No Cointegration",
alternative: str = "Cointegration",
trend: str = "c",
order: int = 2,
xsection: Optional[RegressionResults] = None,
test_type: str = "Za",
kernel_est: Optional[lrcov.CovarianceEstimator] = None,
rho: float = 0.0,
) -> None:
super().__init__(stat,
pvalue,
crit_vals,
null,
alternative,
trend,
order,
xsection=xsection)
self.name = f"Phillips-Ouliaris {test_type} Cointegration Test"
self._test_type = test_type
assert kernel_est is not None
self._kernel_est = kernel_est
self._rho = rho
self._additional_info.update({
"Kernel":
self.kernel,
"Bandwidth":
str_format(kernel_est.bandwidth),
"Trend":
self.trend,
"Distribution Order":
self.distribution_order,
})