# TODO: return forecast errors and confidence intervals
#from sm2.tsa.arima_process import arma2ma
#ma_rep = arma2ma(np.r_[1,-params[::-1]], [1], out_of_sample)
#fcasterr = np.sqrt(self.sigma2 * np.cumsum(ma_rep**2))
preddoc = AR.predict.__doc__.split('\n')
extra_doc = (""" confint : bool, float
Whether to return confidence intervals. If `confint` == True,
95 % confidence intervals are returned. Else if `confint` is a
float, then it is assumed to be the alpha value of the confidence
interval. That is confint == .05 returns a 95% confidence
interval, and .10 would return a 90% confidence interval."""
).split('\n')
#ret_doc = """
# fcasterr : array-like
# confint : array-like
#"""
predict.__doc__ = '\n'.join(preddoc[:5] + preddoc[7:20] + extra_doc +
preddoc[20:])
# TODO: is the docstring inaccurate? It looks like confint isnt returned
class ARResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(
tsa_model.TimeSeriesResultsWrapper._wrap_attrs, _attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(
tsa_model.TimeSeriesResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(ARResultsWrapper, ARResults) # noqa:E305
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
cov_type : string
The type of covariance matrix estimator to use. Can be one of 'approx',
'opg', 'robust', or 'none'.
Attributes
----------
model : Model instance
A reference to the model that was fit.
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
nobs : float
The number of observations used to fit the model.
params : array
The parameters of the model.
scale : float
This is currently set to 1.0 and not used by the model or its results.
"""
pass
class MarkovRegressionResultsWrapper(
markov_switching.MarkovSwitchingResultsWrapper):
pass
wrap.populate_wrapper(
MarkovRegressionResultsWrapper, # noqa:E305
MarkovRegressionResults)
# TODO: I don't like that self.data is mutable
return self.data.xnames
@exog_names.setter
def exog_names(self, vals):
# overwrite with writable property for (V)AR models
if not isinstance(vals, list):
vals = [vals]
self.data.xnames = vals
class TimeSeriesModelResults(base.LikelihoodModelResults):
def __init__(self, model, params, normalized_cov_params, scale=1.):
self.data = model.data
super(TimeSeriesModelResults,
self).__init__(model, params, normalized_cov_params, scale)
class TimeSeriesResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_attrs,
_attrs)
_methods = {'predict': 'dates'}
_wrap_methods = wrap.union_dicts(
base.LikelihoodResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(
TimeSeriesResultsWrapper, # noqa:E305
TimeSeriesModelResults)
else:
return np.nan
# If the new point is not uphill for the target function, take it
# and return. This check is a bit expensive and un-necessary for
# OLS
if not check_step:
return x + h
f1 = func(x + h, model) + L1_wt * np.abs(x + h)
if f1 <= f + L1_wt * np.abs(x) + 1e-10:
return x + h
# Fallback for models where the loss is not quadratic
from scipy.optimize import brent
x_opt = brent(func, args=(model,), brack=(x - 1, x + 1), tol=tol)
return x_opt
class RegularizedResults(Results):
pass
class RegularizedResultsWrapper(wrap.ResultsWrapper):
_attrs = {'params': 'columns',
'resid': 'rows',
'fittedvalues': 'rows'}
_wrap_attrs = _attrs
wrap.populate_wrapper(RegularizedResultsWrapper, # noqa:E305
RegularizedResults)
class L1ZeroInflatedNegativeBinomialResults(L1CountResults,
ZeroInflatedNegativeBinomialResults
): # noqa:E128
pass
# -------------------------------------------------------------
# Wrapper Classes
class ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(
ZeroInflatedPoissonResultsWrapper, # noqa:E305
ZeroInflatedPoissonResults)
class L1ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(
L1ZeroInflatedPoissonResultsWrapper, # noqa:E305
L1ZeroInflatedPoissonResults)
class ZeroInflatedGeneralizedPoissonResultsWrapper(lm.RegressionResultsWrapper
):
pass
smry = Summary()
smry.add_table_2cols(self,
gleft=top_left,
gright=top_right,
yname=yname,
xname=xname,
title=title)
smry.add_table_params(self,
yname=yname,
xname=xname,
alpha=alpha,
use_t=self.use_t)
# add warnings/notes, added to text format only
etext = []
wstr = ("If the model instance has been used for another "
"fit with different fit\n"
"parameters, then the fit options might not be the "
"correct ones anymore .")
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
class RLMResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(RLMResultsWrapper, RLMResults) # noqa:E305