Source code for statsmodels.regression.recursive_ls

"""
Recursive least squares model

Author: Chad Fulton
License: Simplified-BSD
"""
from __future__ import division, absolute_import, print_function

from warnings import warn
from statsmodels.compat.collections import OrderedDict

import numpy as np
import pandas as pd
from statsmodels.regression.linear_model import OLS
from statsmodels.tools.data import _is_using_pandas
from statsmodels.tsa.statespace.mlemodel import (
    MLEModel, MLEResults, MLEResultsWrapper)
from statsmodels.tools.tools import Bunch
from statsmodels.tools.decorators import cache_readonly, resettable_cache
import statsmodels.base.wrapper as wrap

# Columns are alpha = 0.1, 0.05, 0.025, 0.01, 0.005
_cusum_squares_scalars = np.array([
    [1.0729830,   1.2238734,  1.3581015,  1.5174271,  1.6276236],
    [-0.6698868, -0.6700069, -0.6701218, -0.6702672, -0.6703724],
    [-0.5816458, -0.7351697, -0.8858694, -1.0847745, -1.2365861]
])


[docs]class RecursiveLS(MLEModel): r""" Recursive least squares Parameters ---------- endog : array_like The observed time-series process :math:`y` exog : array_like Array of exogenous regressors, shaped nobs x k. Notes ----- Recursive least squares (RLS) corresponds to expanding window ordinary least squares (OLS). This model applies the Kalman filter to compute recursive estimates of the coefficients and recursive residuals. References ---------- .. [1] Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press. """ def __init__(self, endog, exog, **kwargs): # Standardize data if not _is_using_pandas(endog, None): endog = np.asanyarray(endog) exog_is_using_pandas = _is_using_pandas(exog, None) if not exog_is_using_pandas: exog = np.asarray(exog) # Make sure we have 2-dimensional array if exog.ndim == 1: if not exog_is_using_pandas: exog = exog[:, None] else: exog = pd.DataFrame(exog) self.k_exog = exog.shape[1] # Handle coefficient initialization # By default, do not calculate likelihood while it is controlled by # diffuse initial conditions. kwargs.setdefault('loglikelihood_burn', self.k_exog) kwargs.setdefault('initialization', 'approximate_diffuse') kwargs.setdefault('initial_variance', 1e9) # Initialize the state space representation super(RecursiveLS, self).__init__( endog, k_states=self.k_exog, exog=exog, **kwargs ) # Setup the state space representation self['design'] = self.exog[:, :, None].T self['transition'] = np.eye(self.k_states) # Notice that the filter output does not depend on the measurement # variance, so we set it here to 1 self['obs_cov', 0, 0] = 1. @classmethod
[docs] def from_formula(cls, formula, data, subset=None): """ Not implemented for state space models """ return super(MLEModel, cls).from_formula(formula, data, subset)
[docs] def fit(self): """ Fits the model by application of the Kalman filter Returns ------- RecursiveLSResults """ # Get the smoother results with an arbitrary measurement variance smoother_results = self.smooth(return_ssm=True) # Compute the MLE of sigma2 (see Harvey, 1989 equation 4.2.5) resid = smoother_results.standardized_forecasts_error[0] sigma2 = (np.inner(resid, resid) / (self.nobs - self.loglikelihood_burn)) # Now construct a results class, where the params are the final # estimates of the regression coefficients self['obs_cov', 0, 0] = sigma2 return self.smooth()
[docs] def filter(self, return_ssm=False, **kwargs): # Get the state space output result = super(RecursiveLS, self).filter([], transformed=True, cov_type='none', return_ssm=True, **kwargs) # Wrap in a results object if not return_ssm: params = result.filtered_state[:, -1] cov_kwds = { 'custom_cov_type': 'nonrobust', 'custom_cov_params': result.filtered_state_cov[:, :, -1], 'custom_description': ('Parameters and covariance matrix' ' estimates are RLS estimates' ' conditional on the entire sample.') } result = RecursiveLSResultsWrapper( RecursiveLSResults(self, params, result, cov_type='custom', cov_kwds=cov_kwds) ) return result
[docs] def smooth(self, return_ssm=False, **kwargs): # Get the state space output result = super(RecursiveLS, self).smooth([], transformed=True, cov_type='none', return_ssm=True, **kwargs) # Wrap in a results object if not return_ssm: params = result.filtered_state[:, -1] cov_kwds = { 'custom_cov_type': 'nonrobust', 'custom_cov_params': result.filtered_state_cov[:, :, -1], 'custom_description': ('Parameters and covariance matrix' ' estimates are RLS estimates' ' conditional on the entire sample.') } result = RecursiveLSResultsWrapper( RecursiveLSResults(self, params, result, cov_type='custom', cov_kwds=cov_kwds) ) return result
@property def param_names(self): return self.exog_names @property def start_params(self): # Only parameter is the measurement disturbance standard deviation return np.zeros(0)
[docs] def update(self, params, **kwargs): """ Update the parameters of the model Updates the representation matrices to fill in the new parameter values. Parameters ---------- params : array_like Array of new parameters. transformed : boolean, optional Whether or not `params` is already transformed. If set to False, `transform_params` is called. Default is True.. Returns ------- params : array_like Array of parameters. """ pass
[docs]class RecursiveLSResults(MLEResults): """ Class to hold results from fitting a recursive least squares model. Parameters ---------- model : RecursiveLS instance The fitted model instance Attributes ---------- specification : dictionary Dictionary including all attributes from the recursive least squares model instance. See Also -------- statsmodels.tsa.statespace.kalman_filter.FilterResults statsmodels.tsa.statespace.mlemodel.MLEResults """ def __init__(self, model, params, filter_results, cov_type='opg', **kwargs): super(RecursiveLSResults, self).__init__( model, params, filter_results, cov_type, **kwargs) self.df_resid = np.inf # attribute required for wald tests # Save _init_kwds self._init_kwds = self.model._get_init_kwds() # Save the model specification self.specification = Bunch(**{ 'k_exog': self.model.k_exog}) @property def recursive_coefficients(self): """ Estimates of regression coefficients, recursively estimated Returns ------- out: Bunch Has the following attributes: - `filtered`: a time series array with the filtered estimate of the component - `filtered_cov`: a time series array with the filtered estimate of the variance/covariance of the component - `smoothed`: a time series array with the smoothed estimate of the component - `smoothed_cov`: a time series array with the smoothed estimate of the variance/covariance of the component - `offset`: an integer giving the offset in the state vector where this component begins """ out = None spec = self.specification start = offset = 0 end = offset + spec.k_exog out = Bunch( filtered=self.filtered_state[start:end], filtered_cov=self.filtered_state_cov[start:end, start:end], smoothed=None, smoothed_cov=None, offset=offset ) if self.smoothed_state is not None: out.smoothed = self.smoothed_state[start:end] if self.smoothed_state_cov is not None: out.smoothed_cov = ( self.smoothed_state_cov[start:end, start:end]) return out @cache_readonly
[docs] def resid_recursive(self): """ Recursive residuals Returns ------- resid_recursive : array_like An array of length `nobs` holding the recursive residuals. Notes ----- The first `k_exog` residuals are typically unreliable due to initialization. """ # See Harvey (1989) section 5.4; he defines the standardized # innovations in 5.4.1, but they have non-unit variance, whereas # the standardized forecast errors assume unit variance. To convert # to Harvey's definition, we need to multiply by the standard # deviation. return (self.filter_results.standardized_forecasts_error.squeeze() * self.filter_results.obs_cov[0, 0]**0.5)
@cache_readonly
[docs] def cusum(self): r""" Cumulative sum of standardized recursive residuals statistics Returns ------- cusum : array_like An array of length `nobs - k_exog` holding the CUSUM statistics. Notes ----- The CUSUM statistic takes the form: .. math:: W_t = \frac{1}{\hat \sigma} \sum_{j=k+1}^t w_j where :math:`w_j` is the recursive residual at time :math:`j` and :math:`\hat \sigma` is the estimate of the standard deviation from the full sample. Excludes the first `k_exog` datapoints. Due to differences in the way :math:`\hat \sigma` is calculated, the output of this function differs slightly from the output in the R package strucchange and the Stata contributed .ado file cusum6. The calculation in this package is consistent with the description of Brown et al. (1975) References ---------- .. [1] Brown, R. L., J. Durbin, and J. M. Evans. 1975. "Techniques for Testing the Constancy of Regression Relationships over Time." Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92. """ llb = self.loglikelihood_burn return (np.cumsum(self.resid_recursive[self.loglikelihood_burn:]) / np.std(self.resid_recursive[llb:], ddof=1))
@cache_readonly
[docs] def cusum_squares(self): r""" Cumulative sum of squares of standardized recursive residuals statistics Returns ------- cusum_squares : array_like An array of length `nobs - k_exog` holding the CUSUM of squares statistics. Notes ----- The CUSUM of squares statistic takes the form: .. math:: s_t = \left ( \sum_{j=k+1}^t w_j^2 \right ) \Bigg / \left ( \sum_{j=k+1}^T w_j^2 \right ) where :math:`w_j` is the recursive residual at time :math:`j`. Excludes the first `k_exog` datapoints. References ---------- .. [1] Brown, R. L., J. Durbin, and J. M. Evans. 1975. "Techniques for Testing the Constancy of Regression Relationships over Time." Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92. """ numer = np.cumsum(self.resid_recursive[self.loglikelihood_burn:]**2) denom = numer[-1] return numer / denom
[docs] def plot_recursive_coefficient(self, variables=0, alpha=0.05, legend_loc='upper left', fig=None, figsize=None): r""" Plot the recursively estimated coefficients on a given variable Parameters ---------- variables : int or str or iterable of int or string, optional Integer index or string name of the variable whose coefficient will be plotted. Can also be an iterable of integers or strings. Default is the first variable. alpha : float, optional The confidence intervals for the coefficient are (1 - alpha) % legend_loc : string, optional The location of the legend in the plot. Default is upper left. fig : Matplotlib Figure instance, optional If given, subplots are created in this figure instead of in a new figure. Note that the grid will be created in the provided figure using `fig.add_subplot()`. figsize : tuple, optional If a figure is created, this argument allows specifying a size. The tuple is (width, height). Notes ----- All plots contain (1 - `alpha`) % confidence intervals. """ # Get variables if isinstance(variables, (int, str)): variables = [variables] k_variables = len(variables) # If a string was given for `variable`, try to get it from exog names exog_names = self.model.exog_names for i in range(k_variables): variable = variables[i] if isinstance(variable, str): variables[i] = exog_names.index(variable) # Create the plot from scipy.stats import norm from statsmodels.graphics.utils import _import_mpl, create_mpl_fig plt = _import_mpl() fig = create_mpl_fig(fig, figsize) for i in range(k_variables): variable = variables[i] ax = fig.add_subplot(k_variables, 1, i + 1) # Get dates, if applicable if hasattr(self.data, 'dates') and self.data.dates is not None: dates = self.data.dates._mpl_repr() else: dates = np.arange(self.nobs) llb = self.loglikelihood_burn # Plot the coefficient coef = self.recursive_coefficients ax.plot(dates[llb:], coef.filtered[variable, llb:], label='Recursive estimates: %s' % exog_names[variable]) # Legend handles, labels = ax.get_legend_handles_labels() # Get the critical value for confidence intervals if alpha is not None: critical_value = norm.ppf(1 - alpha / 2.) # Plot confidence intervals std_errors = np.sqrt(coef.filtered_cov[variable, variable, :]) ci_lower = ( coef.filtered[variable] - critical_value * std_errors) ci_upper = ( coef.filtered[variable] + critical_value * std_errors) ci_poly = ax.fill_between( dates[llb:], ci_lower[llb:], ci_upper[llb:], alpha=0.2 ) ci_label = ('$%.3g \\%%$ confidence interval' % ((1 - alpha)*100)) # Only add CI to legend for the first plot if i == 0: # Proxy artist for fill_between legend entry # See http://matplotlib.org/1.3.1/users/legend_guide.html p = plt.Rectangle((0, 0), 1, 1, fc=ci_poly.get_facecolor()[0]) handles.append(p) labels.append(ci_label) ax.legend(handles, labels, loc=legend_loc) # Remove xticks for all but the last plot if i < k_variables - 1: ax.xaxis.set_ticklabels([]) fig.tight_layout() return fig
def _cusum_significance_bounds(self, alpha, ddof=0, points=None): """ Parameters ---------- alpha : float, optional The significance bound is alpha %. ddof : int, optional The number of periods additional to `k_exog` to exclude in constructing the bounds. Default is zero. This is usually used only for testing purposes. points : iterable, optional The points at which to evaluate the significance bounds. Default is two points, beginning and end of the sample. Notes ----- Comparing against the cusum6 package for Stata, this does not produce exactly the same confidence bands (which are produced in cusum6 by lw, uw) because they burn the first k_exog + 1 periods instead of the first k_exog. If this change is performed (so that `tmp = (self.nobs - llb - 1)**0.5`), then the output here matches cusum6. The cusum6 behavior does not seem to be consistent with Brown et al. (1975); it is likely they did that because they needed three initial observations to get the initial OLS estimates, whereas we do not need to do that. """ # Get the constant associated with the significance level if alpha == 0.01: scalar = 1.143 elif alpha == 0.05: scalar = 0.948 elif alpha == 0.10: scalar = 0.950 else: raise ValueError('Invalid significance level.') # Get the points for the significance bound lines llb = self.loglikelihood_burn tmp = (self.nobs - llb - ddof)**0.5 upper_line = lambda x: scalar * tmp + 2 * scalar * (x - llb) / tmp if points is None: points = np.array([llb, self.nobs]) return -upper_line(points), upper_line(points)
[docs] def plot_cusum(self, alpha=0.05, legend_loc='upper left', fig=None, figsize=None): r""" Plot the CUSUM statistic and significance bounds. Parameters ---------- alpha : float, optional The plotted significance bounds are alpha %. legend_loc : string, optional The location of the legend in the plot. Default is upper left. fig : Matplotlib Figure instance, optional If given, subplots are created in this figure instead of in a new figure. Note that the grid will be created in the provided figure using `fig.add_subplot()`. figsize : tuple, optional If a figure is created, this argument allows specifying a size. The tuple is (width, height). Notes ----- Evidence of parameter instability may be found if the CUSUM statistic moves out of the significance bounds. References ---------- .. [1] Brown, R. L., J. Durbin, and J. M. Evans. 1975. "Techniques for Testing the Constancy of Regression Relationships over Time." Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92. """ # Create the plot from statsmodels.graphics.utils import _import_mpl, create_mpl_fig plt = _import_mpl() fig = create_mpl_fig(fig, figsize) ax = fig.add_subplot(1, 1, 1) # Get dates, if applicable if hasattr(self.data, 'dates') and self.data.dates is not None: dates = self.data.dates._mpl_repr() else: dates = np.arange(self.nobs) llb = self.loglikelihood_burn # Plot cusum series and reference line ax.plot(dates[llb:], self.cusum, label='CUSUM') ax.hlines(0, dates[llb], dates[-1], color='k', alpha=0.3) # Plot significance bounds lower_line, upper_line = self._cusum_significance_bounds(alpha) ax.plot([dates[llb], dates[-1]], upper_line, 'k--', label='%d%% significance' % (alpha * 100)) ax.plot([dates[llb], dates[-1]], lower_line, 'k--') ax.legend(loc=legend_loc) return fig
def _cusum_squares_significance_bounds(self, alpha, points=None): """ Notes ----- Comparing against the cusum6 package for Stata, this does not produce exactly the same confidence bands (which are produced in cusum6 by lww, uww) because they use a different method for computing the critical value; in particular, they use tabled values from Table C, pp. 364-365 of "The Econometric Analysis of Time Series" Harvey, (1990), and use the value given to 99 observations for any larger number of observations. In contrast, we use the approximating critical values suggested in Edgerton and Wells (1994) which allows computing relatively good approximations for any number of observations. """ # Get the approximate critical value associated with the significance # level llb = self.loglikelihood_burn n = 0.5 * (self.nobs - llb) - 1 try: ix = [0.1, 0.05, 0.025, 0.01, 0.005].index(alpha / 2) except ValueError: raise ValueError('Invalid significance level.') scalars = _cusum_squares_scalars[:, ix] crit = scalars[0] / n**0.5 + scalars[1] / n + scalars[2] / n**1.5 # Get the points for the significance bound lines if points is None: points = np.array([llb, self.nobs]) line = (points - llb) / (self.nobs - llb) return line - crit, line + crit
[docs] def plot_cusum_squares(self, alpha=0.05, legend_loc='upper left', fig=None, figsize=None): r""" Plot the CUSUM of squares statistic and significance bounds. Parameters ---------- alpha : float, optional The plotted significance bounds are alpha %. legend_loc : string, optional The location of the legend in the plot. Default is upper left. fig : Matplotlib Figure instance, optional If given, subplots are created in this figure instead of in a new figure. Note that the grid will be created in the provided figure using `fig.add_subplot()`. figsize : tuple, optional If a figure is created, this argument allows specifying a size. The tuple is (width, height). Notes ----- Evidence of parameter instability may be found if the CUSUM of squares statistic moves out of the significance bounds. Critical values used in creating the significance bounds are computed using the approximate formula of [2]_. References ---------- .. [1] Brown, R. L., J. Durbin, and J. M. Evans. 1975. "Techniques for Testing the Constancy of Regression Relationships over Time." Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92. .. [2] Edgerton, David, and Curt Wells. 1994. "Critical Values for the Cusumsq Statistic in Medium and Large Sized Samples." Oxford Bulletin of Economics and Statistics 56 (3): 355-65. """ # Create the plot from statsmodels.graphics.utils import _import_mpl, create_mpl_fig plt = _import_mpl() fig = create_mpl_fig(fig, figsize) ax = fig.add_subplot(1, 1, 1) # Get dates, if applicable if hasattr(self.data, 'dates') and self.data.dates is not None: dates = self.data.dates._mpl_repr() else: dates = np.arange(self.nobs) llb = self.loglikelihood_burn # Plot cusum series and reference line ax.plot(dates[llb:], self.cusum_squares, label='CUSUM of squares') ref_line = (np.arange(llb, self.nobs) - llb) / (self.nobs - llb) ax.plot(dates[llb:], ref_line, 'k', alpha=0.3) # Plot significance bounds lower_line, upper_line = self._cusum_squares_significance_bounds(alpha) ax.plot([dates[llb], dates[-1]], upper_line, 'k--', label='%d%% significance' % (alpha * 100)) ax.plot([dates[llb], dates[-1]], lower_line, 'k--') ax.legend(loc=legend_loc) return fig
class RecursiveLSResultsWrapper(MLEResultsWrapper): _attrs = {} _wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs, _attrs) _methods = {} _wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods, _methods) wrap.populate_wrapper(RecursiveLSResultsWrapper, RecursiveLSResults)