#!/usr/bin/env python

# DO NOT EDIT
# Autogenerated from the notebook gls.ipynb.
# Edit the notebook and then sync the output with this file.
#
# flake8: noqa
# DO NOT EDIT

# # Generalized Least Squares

import numpy as np

import statsmodels.api as sm

# The Longley dataset is a time series dataset:

data = sm.datasets.longley.load()
data.exog = sm.add_constant(data.exog)
print(data.exog.head())

#
#  Let's assume that the data is heteroskedastic and that we know
#  the nature of the heteroskedasticity.  We can then define
#  `sigma` and use it to give us a GLS model
#
#  First we will obtain the residuals from an OLS fit

ols_resid = sm.OLS(data.endog, data.exog).fit().resid

# Assume that the error terms follow an AR(1) process with a trend:
#
# $\epsilon_i = \beta_0 + \rho\epsilon_{i-1} + \eta_i$
#
# where $\eta \sim N(0,\Sigma^2)$
#
# and that $\rho$ is simply the correlation of the residual a consistent
# estimator for rho is to regress the residuals on the lagged residuals

resid_fit = sm.OLS(
    np.asarray(ols_resid)[1:],
    sm.add_constant(np.asarray(ols_resid)[:-1])).fit()
print(resid_fit.tvalues[1])
print(resid_fit.pvalues[1])

#  While we do not have strong evidence that the errors follow an AR(1)
#  process we continue

rho = resid_fit.params[1]

# As we know, an AR(1) process means that near-neighbors have a stronger
#  relation so we can give this structure by using a toeplitz matrix

from scipy.linalg import toeplitz

toeplitz(range(5))

order = toeplitz(range(len(ols_resid)))

# so that our error covariance structure is actually rho**order
#  which defines an autocorrelation structure

sigma = rho**order
gls_model = sm.GLS(data.endog, data.exog, sigma=sigma)
gls_results = gls_model.fit()

# Of course, the exact rho in this instance is not known so it it might
# make more sense to use feasible gls, which currently only has experimental
# support.
#
# We can use the GLSAR model with one lag, to get to a similar result:

glsar_model = sm.GLSAR(data.endog, data.exog, 1)
glsar_results = glsar_model.iterative_fit(1)
print(glsar_results.summary())

# Comparing gls and glsar results, we see that there are some small
#  differences in the parameter estimates and the resulting standard
#  errors of the parameter estimate. This might be do to the numerical
#  differences in the algorithm, e.g. the treatment of initial conditions,
#  because of the small number of observations in the longley dataset.

print(gls_results.params)
print(glsar_results.params)
print(gls_results.bse)
print(glsar_results.bse)
