Source code for statsmodels.tsa.statespace.dynamic_factor

# -*- coding: utf-8 -*-
"""
Dynamic factor model

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

from warnings import warn
from collections import OrderedDict

import numpy as np
from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
from .tools import (
    is_invertible, prepare_exog,
    constrain_stationary_univariate, unconstrain_stationary_univariate,
    constrain_stationary_multivariate, unconstrain_stationary_multivariate
)
from statsmodels.multivariate.pca import PCA
from statsmodels.regression.linear_model import OLS
from statsmodels.tsa.vector_ar.var_model import VAR
from statsmodels.tools.tools import Bunch
from statsmodels.tools.data import _is_using_pandas
from statsmodels.tsa.tsatools import lagmat
from statsmodels.tools.decorators import cache_readonly
from statsmodels.tools.sm_exceptions import ValueWarning
import statsmodels.base.wrapper as wrap


[docs]class DynamicFactor(MLEModel): r""" Dynamic factor model Parameters ---------- endog : array_like The observed time-series process :math:`y` exog : array_like, optional Array of exogenous regressors for the observation equation, shaped nobs x k_exog. k_factors : int The number of unobserved factors. factor_order : int The order of the vector autoregression followed by the factors. error_cov_type : {'scalar', 'diagonal', 'unstructured'}, optional The structure of the covariance matrix of the observation error term, where "unstructured" puts no restrictions on the matrix, "diagonal" requires it to be any diagonal matrix (uncorrelated errors), and "scalar" requires it to be a scalar times the identity matrix. Default is "diagonal". error_order : int, optional The order of the vector autoregression followed by the observation error component. Default is None, corresponding to white noise errors. error_var : boolean, optional Whether or not to model the errors jointly via a vector autoregression, rather than as individual autoregressions. Has no effect unless `error_order` is set. Default is False. enforce_stationarity : boolean, optional Whether or not to transform the AR parameters to enforce stationarity in the autoregressive component of the model. Default is True. **kwargs Keyword arguments may be used to provide default values for state space matrices or for Kalman filtering options. See `Representation`, and `KalmanFilter` for more details. Attributes ---------- exog : array_like, optional Array of exogenous regressors for the observation equation, shaped nobs x k_exog. k_factors : int The number of unobserved factors. factor_order : int The order of the vector autoregression followed by the factors. error_cov_type : {'diagonal', 'unstructured'} The structure of the covariance matrix of the error term, where "unstructured" puts no restrictions on the matrix and "diagonal" requires it to be a diagonal matrix (uncorrelated errors). error_order : int The order of the vector autoregression followed by the observation error component. error_var : boolean Whether or not to model the errors jointly via a vector autoregression, rather than as individual autoregressions. Has no effect unless `error_order` is set. enforce_stationarity : boolean, optional Whether or not to transform the AR parameters to enforce stationarity in the autoregressive component of the model. Default is True. Notes ----- The dynamic factor model considered here is in the so-called static form, and is specified: .. math:: y_t & = \Lambda f_t + B x_t + u_t \\ f_t & = A_1 f_{t-1} + \dots + A_p f_{t-p} + \eta_t \\ u_t & = C_1 u_{t-1} + \dots + C_1 f_{t-q} + \varepsilon_t where there are `k_endog` observed series and `k_factors` unobserved factors. Thus :math:`y_t` is a `k_endog` x 1 vector and :math:`f_t` is a `k_factors` x 1 vector. :math:`x_t` are optional exogenous vectors, shaped `k_exog` x 1. :math:`\eta_t` and :math:`\varepsilon_t` are white noise error terms. In order to identify the factors, :math:`Var(\eta_t) = I`. Denote :math:`Var(\varepsilon_t) \equiv \Sigma`. Options related to the unobserved factors: - `k_factors`: this is the dimension of the vector :math:`f_t`, above. To exclude factors completely, set `k_factors = 0`. - `factor_order`: this is the number of lags to include in the factor evolution equation, and corresponds to :math:`p`, above. To have static factors, set `factor_order = 0`. Options related to the observation error term :math:`u_t`: - `error_order`: the number of lags to include in the error evolution equation; corresponds to :math:`q`, above. To have white noise errors, set `error_order = 0` (this is the default). - `error_cov_type`: this controls the form of the covariance matrix :math:`\Sigma`. If it is "dscalar", then :math:`\Sigma = \sigma^2 I`. If it is "diagonal", then :math:`\Sigma = \text{diag}(\sigma_1^2, \dots, \sigma_n^2)`. If it is "unstructured", then :math:`\Sigma` is any valid variance / covariance matrix (i.e. symmetric and positive definite). - `error_var`: this controls whether or not the errors evolve jointly according to a VAR(q), or individually according to separate AR(q) processes. In terms of the formulation above, if `error_var = False`, then the matrices :math:C_i` are diagonal, otherwise they are general VAR matrices. References ---------- .. [*] L├╝tkepohl, Helmut. 2007. New Introduction to Multiple Time Series Analysis. Berlin: Springer. """ def __init__(self, endog, k_factors, factor_order, exog=None, error_order=0, error_var=False, error_cov_type='diagonal', enforce_stationarity=True, **kwargs): # Model properties self.enforce_stationarity = enforce_stationarity # Factor-related properties self.k_factors = k_factors self.factor_order = factor_order # Error-related properties self.error_order = error_order self.error_var = error_var and error_order > 0 self.error_cov_type = error_cov_type # Exogenous data (self.k_exog, exog) = prepare_exog(exog) # Note: at some point in the future might add state regression, as in # SARIMAX. self.mle_regression = self.k_exog > 0 # We need to have an array or pandas at this point if not _is_using_pandas(endog, None): endog = np.asanyarray(endog, order='C') # Save some useful model orders, internally used k_endog = endog.shape[1] if endog.ndim > 1 else 1 self._factor_order = max(1, self.factor_order) * self.k_factors self._error_order = self.error_order * k_endog # Calculate the number of states k_states = self._factor_order k_posdef = self.k_factors if self.error_order > 0: k_states += self._error_order k_posdef += k_endog if k_states == 0: k_states = 1 k_posdef = 1 # Test for non-multivariate endog if k_endog < 2: raise ValueError('The dynamic factors model is only valid for' ' multivariate time series.') # Test for too many factors if self.k_factors >= k_endog: raise ValueError('Number of factors must be less than the number' ' of endogenous variables.') # Test for invalid error_cov_type if self.error_cov_type not in ['scalar', 'diagonal', 'unstructured']: raise ValueError('Invalid error covariance matrix type' ' specification.') # By default, initialize as stationary kwargs.setdefault('initialization', 'stationary') # Initialize the state space model super(DynamicFactor, self).__init__( endog, exog=exog, k_states=k_states, k_posdef=k_posdef, **kwargs ) # Set as time-varying model if we have exog if self.k_exog > 0: self.ssm._time_invariant = False # Initialize the components self.parameters = OrderedDict() self._initialize_loadings() self._initialize_exog() self._initialize_error_cov() self._initialize_factor_transition() self._initialize_error_transition() self.k_params = sum(self.parameters.values()) # Cache parameter vector slices def _slice(key, offset): length = self.parameters[key] param_slice = np.s_[offset:offset + length] offset += length return param_slice, offset offset = 0 self._params_loadings, offset = _slice('factor_loadings', offset) self._params_exog, offset = _slice('exog', offset) self._params_error_cov, offset = _slice('error_cov', offset) self._params_factor_transition, offset = ( _slice('factor_transition', offset)) self._params_error_transition, offset = ( _slice('error_transition', offset)) def _initialize_loadings(self): # Initialize the parameters self.parameters['factor_loadings'] = self.k_endog * self.k_factors # Setup fixed components of state space matrices if self.error_order > 0: start = self._factor_order end = self._factor_order + self.k_endog self.ssm['design', :, start:end] = np.eye(self.k_endog) # Setup indices of state space matrices self._idx_loadings = np.s_['design', :, :self.k_factors] def _initialize_exog(self): # Initialize the parameters self.parameters['exog'] = self.k_exog * self.k_endog # If we have exog effects, then the obs intercept needs to be # time-varying if self.k_exog > 0: self.ssm['obs_intercept'] = np.zeros((self.k_endog, self.nobs)) # Setup indices of state space matrices self._idx_exog = np.s_['obs_intercept', :self.k_endog, :] def _initialize_error_cov(self): if self.error_cov_type == 'scalar': self._initialize_error_cov_diagonal(scalar=True) elif self.error_cov_type == 'diagonal': self._initialize_error_cov_diagonal(scalar=False) elif self.error_cov_type == 'unstructured': self._initialize_error_cov_unstructured() def _initialize_error_cov_diagonal(self, scalar=False): # Initialize the parameters self.parameters['error_cov'] = 1 if scalar else self.k_endog # Setup fixed components of state space matrices # Setup indices of state space matrices k_endog = self.k_endog k_factors = self.k_factors idx = np.diag_indices(k_endog) if self.error_order > 0: matrix = 'state_cov' idx = (idx[0] + k_factors, idx[1] + k_factors) else: matrix = 'obs_cov' self._idx_error_cov = (matrix,) + idx def _initialize_error_cov_unstructured(self): # Initialize the parameters k_endog = self.k_endog self.parameters['error_cov'] = int(k_endog * (k_endog + 1) / 2) # Setup fixed components of state space matrices # Setup indices of state space matrices self._idx_lower_error_cov = np.tril_indices(self.k_endog) if self.error_order > 0: start = self.k_factors end = self.k_factors + self.k_endog self._idx_error_cov = ( np.s_['state_cov', start:end, start:end]) else: self._idx_error_cov = np.s_['obs_cov', :, :] def _initialize_factor_transition(self): order = self.factor_order * self.k_factors k_factors = self.k_factors # Initialize the parameters self.parameters['factor_transition'] = ( self.factor_order * self.k_factors**2) # Setup fixed components of state space matrices # VAR(p) for factor transition if self.k_factors > 0: if self.factor_order > 0: self.ssm['transition', k_factors:order, :order - k_factors] = ( np.eye(order - k_factors)) self.ssm['selection', :k_factors, :k_factors] = np.eye(k_factors) # Identification requires constraining the state covariance to an # identity matrix self.ssm['state_cov', :k_factors, :k_factors] = np.eye(k_factors) # Setup indices of state space matrices self._idx_factor_transition = np.s_['transition', :k_factors, :order] def _initialize_error_transition(self): # Initialize the appropriate situation if self.error_order == 0: self._initialize_error_transition_white_noise() else: # Generic setup fixed components of state space matrices # VAR(q) for error transition # (in the individual AR case, we still have the VAR(q) companion # matrix structure, but force the coefficient matrices to be # diagonal) k_endog = self.k_endog k_factors = self.k_factors _factor_order = self._factor_order _error_order = self._error_order _slice = np.s_['selection', _factor_order:_factor_order + k_endog, k_factors:k_factors + k_endog] self.ssm[_slice] = np.eye(k_endog) _slice = np.s_[ 'transition', _factor_order + k_endog:_factor_order + _error_order, _factor_order:_factor_order + _error_order - k_endog] self.ssm[_slice] = np.eye(_error_order - k_endog) # Now specialized setups if self.error_var: self._initialize_error_transition_var() else: self._initialize_error_transition_individual() def _initialize_error_transition_white_noise(self): # Initialize the parameters self.parameters['error_transition'] = 0 # No fixed components of state space matrices # Setup indices of state space matrices (just an empty slice) self._idx_error_transition = np.s_['transition', 0:0, 0:0] def _initialize_error_transition_var(self): k_endog = self.k_endog _factor_order = self._factor_order _error_order = self._error_order # Initialize the parameters self.parameters['error_transition'] = _error_order * k_endog # Fixed components already setup above # Setup indices of state space matrices # Here we want to set all of the elements of the coefficient matrices, # the same as in a VAR specification self._idx_error_transition = np.s_[ 'transition', _factor_order:_factor_order + k_endog, _factor_order:_factor_order + _error_order] def _initialize_error_transition_individual(self): k_endog = self.k_endog _error_order = self._error_order # Initialize the parameters self.parameters['error_transition'] = _error_order # Fixed components already setup above # Setup indices of state space matrices # Here we want to set only the diagonal elements of the coefficient # matrices, and we want to set them in order by equation, not by # matrix (i.e. set the first element of the first matrix's diagonal, # then set the first element of the second matrix's diagonal, then...) # The basic setup is a tiled list of diagonal indices, one for each # coefficient matrix idx = np.tile(np.diag_indices(k_endog), self.error_order) # Now we need to shift the rows down to the correct location row_shift = self._factor_order # And we need to shift the columns in an increasing way col_inc = self._factor_order + np.repeat( [i * k_endog for i in range(self.error_order)], k_endog) idx[0] += row_shift idx[1] += col_inc # Make a copy (without the row shift) so that we can easily get the # diagonal parameters back out of a generic coefficients matrix array idx_diag = idx.copy() idx_diag[0] -= row_shift idx_diag[1] -= self._factor_order idx_diag = idx_diag[:, np.lexsort((idx_diag[1], idx_diag[0]))] self._idx_error_diag = (idx_diag[0], idx_diag[1]) # Finally, we want to fill the entries in in the correct order, which # is to say we want to fill in lexicographically, first by row then by # column idx = idx[:, np.lexsort((idx[1], idx[0]))] self._idx_error_transition = np.s_['transition', idx[0], idx[1]] @property def _res_classes(self): return {'fit': (DynamicFactorResults, DynamicFactorResultsWrapper)} @property def start_params(self): params = np.zeros(self.k_params, dtype=np.float64) endog = self.endog.copy() # 1. Factor loadings (estimated via PCA) if self.k_factors > 0: # Use principal components + OLS as starting values res_pca = PCA(endog, ncomp=self.k_factors) mod_ols = OLS(endog, res_pca.factors) res_ols = mod_ols.fit() # Using OLS params for the loadings tends to gives higher starting # log-likelihood. params[self._params_loadings] = res_ols.params.T.ravel() # params[self._params_loadings] = res_pca.loadings.ravel() # However, using res_ols.resid tends to causes non-invertible # starting VAR coefficients for error VARs # endog = res_ols.resid endog = endog - np.dot(res_pca.factors, res_pca.loadings.T) # 2. Exog (OLS on residuals) if self.k_exog > 0: mod_ols = OLS(endog, exog=self.exog) res_ols = mod_ols.fit() # In the form: beta.x1.y1, beta.x2.y1, beta.x1.y2, ... params[self._params_exog] = res_ols.params.T.ravel() endog = res_ols.resid # 3. Factors (VAR on res_pca.factors) stationary = True if self.k_factors > 1 and self.factor_order > 0: # 3a. VAR transition (OLS on factors estimated via PCA) mod_factors = VAR(res_pca.factors) res_factors = mod_factors.fit(maxlags=self.factor_order, ic=None, trend='nc') # Save the parameters params[self._params_factor_transition] = ( res_factors.params.T.ravel()) # Test for stationarity coefficient_matrices = ( params[self._params_factor_transition].reshape( self.k_factors * self.factor_order, self.k_factors ).T ).reshape(self.k_factors, self.k_factors, self.factor_order).T stationary = is_invertible([1] + list(-coefficient_matrices)) elif self.k_factors > 0 and self.factor_order > 0: # 3b. AR transition Y = res_pca.factors[self.factor_order:] X = lagmat(res_pca.factors, self.factor_order, trim='both') params_ar = np.linalg.pinv(X).dot(Y) stationary = is_invertible(np.r_[1, -params_ar.squeeze()]) params[self._params_factor_transition] = params_ar[:, 0] # Check for stationarity if not stationary and self.enforce_stationarity: raise ValueError('Non-stationary starting autoregressive' ' parameters found with `enforce_stationarity`' ' set to True.') # 4. Errors if self.error_order == 0: if self.error_cov_type == 'scalar': params[self._params_error_cov] = endog.var(axis=0).mean() elif self.error_cov_type == 'diagonal': params[self._params_error_cov] = endog.var(axis=0) elif self.error_cov_type == 'unstructured': cov_factor = np.diag(endog.std(axis=0)) params[self._params_error_cov] = ( cov_factor[self._idx_lower_error_cov].ravel()) else: mod_errors = VAR(endog) res_errors = mod_errors.fit(maxlags=self.error_order, ic=None, trend='nc') # Test for stationarity coefficient_matrices = ( np.array(res_errors.params.T).ravel().reshape( self.k_endog * self.error_order, self.k_endog ).T ).reshape(self.k_endog, self.k_endog, self.error_order).T stationary = is_invertible([1] + list(-coefficient_matrices)) if not stationary and self.enforce_stationarity: raise ValueError('Non-stationary starting error autoregressive' ' parameters found with' ' `enforce_stationarity` set to True.') # Get the error autoregressive parameters if self.error_var: params[self._params_error_transition] = ( np.array(res_errors.params.T).ravel()) else: # In the case of individual autoregressions, extract just the # diagonal elements params[self._params_error_transition] = ( res_errors.params.T[self._idx_error_diag]) # Get the error covariance parameters if self.error_cov_type == 'scalar': params[self._params_error_cov] = ( res_errors.sigma_u.diagonal().mean()) elif self.error_cov_type == 'diagonal': params[self._params_error_cov] = res_errors.sigma_u.diagonal() elif self.error_cov_type == 'unstructured': try: cov_factor = np.linalg.cholesky(res_errors.sigma_u) except np.linalg.LinAlgError: cov_factor = np.eye(res_errors.sigma_u.shape[0]) * ( res_errors.sigma_u.diagonal().mean()**0.5) cov_factor = np.eye(res_errors.sigma_u.shape[0]) * ( res_errors.sigma_u.diagonal().mean()**0.5) params[self._params_error_cov] = ( cov_factor[self._idx_lower_error_cov].ravel()) return params @property def param_names(self): param_names = [] endog_names = self.endog_names # 1. Factor loadings param_names += [ 'loading.f%d.%s' % (j+1, endog_names[i]) for i in range(self.k_endog) for j in range(self.k_factors) ] # 2. Exog # Recall these are in the form: beta.x1.y1, beta.x2.y1, beta.x1.y2, ... param_names += [ 'beta.%s.%s' % (self.exog_names[j], endog_names[i]) for i in range(self.k_endog) for j in range(self.k_exog) ] # 3. Error covariances if self.error_cov_type == 'scalar': param_names += ['sigma2'] elif self.error_cov_type == 'diagonal': param_names += [ 'sigma2.%s' % endog_names[i] for i in range(self.k_endog) ] elif self.error_cov_type == 'unstructured': param_names += [ ('sqrt.var.%s' % endog_names[i] if i == j else 'sqrt.cov.%s.%s' % (endog_names[j], endog_names[i])) for i in range(self.k_endog) for j in range(i+1) ] # 4. Factor transition VAR param_names += [ 'L%d.f%d.f%d' % (i+1, k+1, j+1) for j in range(self.k_factors) for i in range(self.factor_order) for k in range(self.k_factors) ] # 5. Error transition VAR if self.error_var: param_names += [ 'L%d.e(%s).e(%s)' % (i+1, endog_names[k], endog_names[j]) for j in range(self.k_endog) for i in range(self.error_order) for k in range(self.k_endog) ] else: param_names += [ 'L%d.e(%s).e(%s)' % (i+1, endog_names[j], endog_names[j]) for j in range(self.k_endog) for i in range(self.error_order) ] return param_names
[docs] def transform_params(self, unconstrained): """ Transform unconstrained parameters used by the optimizer to constrained parameters used in likelihood evaluation Parameters ---------- unconstrained : array_like Array of unconstrained parameters used by the optimizer, to be transformed. Returns ------- constrained : array_like Array of constrained parameters which may be used in likelihood evalation. Notes ----- Constrains the factor transition to be stationary and variances to be positive. """ unconstrained = np.array(unconstrained, ndmin=1) dtype = unconstrained.dtype constrained = np.zeros(unconstrained.shape, dtype=dtype) # 1. Factor loadings # The factor loadings do not need to be adjusted constrained[self._params_loadings] = ( unconstrained[self._params_loadings]) # 2. Exog # The regression coefficients do not need to be adjusted constrained[self._params_exog] = ( unconstrained[self._params_exog]) # 3. Error covariances # If we have variances, force them to be positive if self.error_cov_type in ['scalar', 'diagonal']: constrained[self._params_error_cov] = ( unconstrained[self._params_error_cov]**2) # Otherwise, nothing needs to be done elif self.error_cov_type == 'unstructured': constrained[self._params_error_cov] = ( unconstrained[self._params_error_cov]) # 4. Factor transition VAR # VAR transition: optionally force to be stationary if self.enforce_stationarity and self.factor_order > 0: # Transform the parameters unconstrained_matrices = ( unconstrained[self._params_factor_transition].reshape( self.k_factors, self._factor_order)) # This is always an identity matrix, but because the transform # done prior to update (where the ssm representation matrices # change), it may be complex cov = self.ssm['state_cov', :self.k_factors, :self.k_factors].real coefficient_matrices, variance = ( constrain_stationary_multivariate(unconstrained_matrices, cov)) constrained[self._params_factor_transition] = ( coefficient_matrices.ravel()) else: constrained[self._params_factor_transition] = ( unconstrained[self._params_factor_transition]) # 5. Error transition VAR # VAR transition: optionally force to be stationary if self.enforce_stationarity and self.error_order > 0: # Joint VAR specification if self.error_var: unconstrained_matrices = ( unconstrained[self._params_error_transition].reshape( self.k_endog, self._error_order)) start = self.k_factors end = self.k_factors + self.k_endog cov = self.ssm['state_cov', start:end, start:end].real coefficient_matrices, variance = ( constrain_stationary_multivariate( unconstrained_matrices, cov)) constrained[self._params_error_transition] = ( coefficient_matrices.ravel()) # Separate AR specifications else: coefficients = ( unconstrained[self._params_error_transition].copy()) for i in range(self.k_endog): start = i * self.error_order end = (i + 1) * self.error_order coefficients[start:end] = constrain_stationary_univariate( coefficients[start:end]) constrained[self._params_error_transition] = coefficients else: constrained[self._params_error_transition] = ( unconstrained[self._params_error_transition]) return constrained
[docs] def untransform_params(self, constrained): """ Transform constrained parameters used in likelihood evaluation to unconstrained parameters used by the optimizer. Parameters ---------- constrained : array_like Array of constrained parameters used in likelihood evalution, to be transformed. Returns ------- unconstrained : array_like Array of unconstrained parameters used by the optimizer. """ constrained = np.array(constrained, ndmin=1) dtype = constrained.dtype unconstrained = np.zeros(constrained.shape, dtype=dtype) # 1. Factor loadings # The factor loadings do not need to be adjusted unconstrained[self._params_loadings] = ( constrained[self._params_loadings]) # 2. Exog # The regression coefficients do not need to be adjusted unconstrained[self._params_exog] = ( constrained[self._params_exog]) # 3. Error covariances # If we have variances, force them to be positive if self.error_cov_type in ['scalar', 'diagonal']: unconstrained[self._params_error_cov] = ( constrained[self._params_error_cov]**0.5) # Otherwise, nothing needs to be done elif self.error_cov_type == 'unstructured': unconstrained[self._params_error_cov] = ( constrained[self._params_error_cov]) # 3. Factor transition VAR # VAR transition: optionally force to be stationary if self.enforce_stationarity and self.factor_order > 0: # Transform the parameters constrained_matrices = ( constrained[self._params_factor_transition].reshape( self.k_factors, self._factor_order)) cov = self.ssm['state_cov', :self.k_factors, :self.k_factors].real coefficient_matrices, variance = ( unconstrain_stationary_multivariate( constrained_matrices, cov)) unconstrained[self._params_factor_transition] = ( coefficient_matrices.ravel()) else: unconstrained[self._params_factor_transition] = ( constrained[self._params_factor_transition]) # 5. Error transition VAR # VAR transition: optionally force to be stationary if self.enforce_stationarity and self.error_order > 0: # Joint VAR specification if self.error_var: constrained_matrices = ( constrained[self._params_error_transition].reshape( self.k_endog, self._error_order)) start = self.k_factors end = self.k_factors + self.k_endog cov = self.ssm['state_cov', start:end, start:end].real coefficient_matrices, variance = ( unconstrain_stationary_multivariate( constrained_matrices, cov)) unconstrained[self._params_error_transition] = ( coefficient_matrices.ravel()) # Separate AR specifications else: coefficients = ( constrained[self._params_error_transition].copy()) for i in range(self.k_endog): start = i * self.error_order end = (i + 1) * self.error_order coefficients[start:end] = ( unconstrain_stationary_univariate( coefficients[start:end])) unconstrained[self._params_error_transition] = coefficients else: unconstrained[self._params_error_transition] = ( constrained[self._params_error_transition]) return unconstrained
[docs] def update(self, params, transformed=True, complex_step=False): """ 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. Notes ----- Let `n = k_endog`, `m = k_factors`, and `p = factor_order`. Then the `params` vector has length :math:`[n \times m] + [n] + [m^2 \times p]`. It is expanded in the following way: - The first :math:`n \times m` parameters fill out the factor loading matrix, starting from the [0,0] entry and then proceeding along rows. These parameters are not modified in `transform_params`. - The next :math:`n` parameters provide variances for the error_cov errors in the observation equation. They fill in the diagonal of the observation covariance matrix, and are constrained to be positive by `transofrm_params`. - The next :math:`m^2 \times p` parameters are used to create the `p` coefficient matrices for the vector autoregression describing the factor transition. They are transformed in `transform_params` to enforce stationarity of the VAR(p). They are placed so as to make the transition matrix a companion matrix for the VAR. In particular, we assume that the first :math:`m^2` parameters fill the first coefficient matrix (starting at [0,0] and filling along rows), the second :math:`m^2` parameters fill the second matrix, etc. """ params = super(DynamicFactor, self).update( params, transformed=transformed, complex_step=complex_step) # 1. Factor loadings # Update the design / factor loading matrix self.ssm[self._idx_loadings] = ( params[self._params_loadings].reshape(self.k_endog, self.k_factors) ) # 2. Exog if self.k_exog > 0: exog_params = params[self._params_exog].reshape( self.k_endog, self.k_exog).T self.ssm[self._idx_exog] = np.dot(self.exog, exog_params).T # 3. Error covariances if self.error_cov_type in ['scalar', 'diagonal']: self.ssm[self._idx_error_cov] = ( params[self._params_error_cov]) elif self.error_cov_type == 'unstructured': error_cov_lower = np.zeros((self.k_endog, self.k_endog), dtype=params.dtype) error_cov_lower[self._idx_lower_error_cov] = ( params[self._params_error_cov]) self.ssm[self._idx_error_cov] = ( np.dot(error_cov_lower, error_cov_lower.T)) # 4. Factor transition VAR self.ssm[self._idx_factor_transition] = ( params[self._params_factor_transition].reshape( self.k_factors, self.factor_order * self.k_factors)) # 5. Error transition VAR if self.error_var: self.ssm[self._idx_error_transition] = ( params[self._params_error_transition].reshape( self.k_endog, self._error_order)) else: self.ssm[self._idx_error_transition] = ( params[self._params_error_transition])
[docs]class DynamicFactorResults(MLEResults): """ Class to hold results from fitting an DynamicFactor model. Parameters ---------- model : DynamicFactor instance The fitted model instance Attributes ---------- specification : dictionary Dictionary including all attributes from the DynamicFactor model instance. coefficient_matrices_var : array Array containing autoregressive lag polynomial coefficient matrices, ordered from lowest degree to highest. 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(DynamicFactorResults, self).__init__(model, params, filter_results, cov_type, **kwargs) self.df_resid = np.inf # attribute required for wald tests self.specification = Bunch(**{ # Model properties 'k_endog': self.model.k_endog, 'enforce_stationarity': self.model.enforce_stationarity, # Factor-related properties 'k_factors': self.model.k_factors, 'factor_order': self.model.factor_order, # Error-related properties 'error_order': self.model.error_order, 'error_var': self.model.error_var, 'error_cov_type': self.model.error_cov_type, # Other properties 'k_exog': self.model.k_exog }) # Polynomials / coefficient matrices self.coefficient_matrices_var = None if self.model.factor_order > 0: ar_params = ( np.array(self.params[self.model._params_factor_transition])) k_factors = self.model.k_factors factor_order = self.model.factor_order self.coefficient_matrices_var = ( ar_params.reshape(k_factors * factor_order, k_factors).T ).reshape(k_factors, k_factors, factor_order).T self.coefficient_matrices_error = None if self.model.error_order > 0: ar_params = ( np.array(self.params[self.model._params_error_transition])) k_endog = self.model.k_endog error_order = self.model.error_order if self.model.error_var: self.coefficient_matrices_error = ( ar_params.reshape(k_endog * error_order, k_endog).T ).reshape(k_endog, k_endog, error_order).T else: mat = np.zeros((k_endog, k_endog * error_order)) mat[self.model._idx_error_diag] = ar_params self.coefficient_matrices_error = ( mat.T.reshape(error_order, k_endog, k_endog)) @property def factors(self): """ Estimates of unobserved factors 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 """ # If present, level is always the first component of the state vector out = None spec = self.specification if spec.k_factors > 0: offset = 0 end = spec.k_factors res = self.filter_results out = Bunch( filtered=res.filtered_state[offset:end], filtered_cov=res.filtered_state_cov[offset:end, offset:end], smoothed=None, smoothed_cov=None, offset=offset) if self.smoothed_state is not None: out.smoothed = self.smoothed_state[offset:end] if self.smoothed_state_cov is not None: out.smoothed_cov = ( self.smoothed_state_cov[offset:end, offset:end]) return out
[docs] @cache_readonly def coefficients_of_determination(self): """ Coefficients of determination (:math:`R^2`) from regressions of individual estimated factors on endogenous variables. Returns ------- coefficients_of_determination : array A `k_endog` x `k_factors` array, where `coefficients_of_determination[i, j]` represents the :math:`R^2` value from a regression of factor `j` and a constant on endogenous variable `i`. Notes ----- Although it can be difficult to interpret the estimated factor loadings and factors, it is often helpful to use the cofficients of determination from univariate regressions to assess the importance of each factor in explaining the variation in each endogenous variable. In models with many variables and factors, this can sometimes lend interpretation to the factors (for example sometimes one factor will load primarily on real variables and another on nominal variables). See Also -------- plot_coefficients_of_determination """ from statsmodels.tools import add_constant spec = self.specification coefficients = np.zeros((spec.k_endog, spec.k_factors)) which = 'filtered' if self.smoothed_state is None else 'smoothed' for i in range(spec.k_factors): exog = add_constant(self.factors[which][i]) for j in range(spec.k_endog): endog = self.filter_results.endog[j] coefficients[j, i] = OLS(endog, exog).fit().rsquared return coefficients
[docs] def plot_coefficients_of_determination(self, endog_labels=None, fig=None, figsize=None): """ Plot the coefficients of determination Parameters ---------- endog_labels : boolean, optional Whether or not to label the endogenous variables along the x-axis of the plots. Default is to include labels if there are 5 or fewer endogenous variables. 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 ----- Produces a `k_factors` x 1 plot grid. The `i`th plot shows a bar plot of the coefficients of determination associated with factor `i`. The endogenous variables are arranged along the x-axis according to their position in the `endog` array. See Also -------- coefficients_of_determination """ from statsmodels.graphics.utils import _import_mpl, create_mpl_fig _import_mpl() fig = create_mpl_fig(fig, figsize) spec = self.specification # Should we label endogenous variables? if endog_labels is None: endog_labels = spec.k_endog <= 5 # Plot the coefficients of determination coefficients_of_determination = self.coefficients_of_determination plot_idx = 1 locations = np.arange(spec.k_endog) for coeffs in coefficients_of_determination.T: # Create the new axis ax = fig.add_subplot(spec.k_factors, 1, plot_idx) ax.set_ylim((0, 1)) ax.set(title='Factor %i' % plot_idx, ylabel=r'$R^2$') bars = ax.bar(locations, coeffs) if endog_labels: width = bars[0].get_width() ax.xaxis.set_ticks(locations + width / 2) ax.xaxis.set_ticklabels(self.model.endog_names) else: ax.set(xlabel='Endogenous variables') ax.xaxis.set_ticks([]) plot_idx += 1 return fig
[docs] def get_prediction(self, start=None, end=None, dynamic=False, index=None, exog=None, **kwargs): """ In-sample prediction and out-of-sample forecasting Parameters ---------- start : int, str, or datetime, optional Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. Default is the the zeroth observation. end : int, str, or datetime, optional Zero-indexed observation number at which to end forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. However, if the dates index does not have a fixed frequency, end must be an integer index if you want out of sample prediction. Default is the last observation in the sample. exog : array_like, optional If the model includes exogenous regressors, you must provide exactly enough out-of-sample values for the exogenous variables if end is beyond the last observation in the sample. dynamic : boolean, int, str, or datetime, optional Integer offset relative to `start` at which to begin dynamic prediction. Can also be an absolute date string to parse or a datetime type (these are not interpreted as offsets). Prior to this observation, true endogenous values will be used for prediction; starting with this observation and continuing through the end of prediction, forecasted endogenous values will be used instead. **kwargs Additional arguments may required for forecasting beyond the end of the sample. See `FilterResults.predict` for more details. Returns ------- forecast : array Array of out of sample forecasts. """ if start is None: start = self.model._index[0] # Handle end (e.g. date) _start, _end, _out_of_sample, prediction_index = ( self.model._get_prediction_index(start, end, index, silent=True)) # Handle exogenous parameters if _out_of_sample and self.model.k_exog > 0: # Create a new faux VARMAX model for the extended dataset nobs = self.model.data.orig_endog.shape[0] + _out_of_sample endog = np.zeros((nobs, self.model.k_endog)) if self.model.k_exog > 0: if exog is None: raise ValueError('Out-of-sample forecasting in a model' ' with a regression component requires' ' additional exogenous values via the' ' `exog` argument.') exog = np.array(exog) required_exog_shape = (_out_of_sample, self.model.k_exog) if not exog.shape == required_exog_shape: raise ValueError('Provided exogenous values are not of the' ' appropriate shape. Required %s, got %s.' % (str(required_exog_shape), str(exog.shape))) exog = np.c_[self.model.data.orig_exog.T, exog.T].T # TODO replace with init_kwds or specification or similar model = DynamicFactor( endog, k_factors=self.model.k_factors, factor_order=self.model.factor_order, exog=exog, error_order=self.model.error_order, error_var=self.model.error_var, error_cov_type=self.model.error_cov_type, enforce_stationarity=self.model.enforce_stationarity ) model.update(self.params) # Set the kwargs with the update time-varying state space # representation matrices for name in self.filter_results.shapes.keys(): if name == 'obs': continue mat = getattr(model.ssm, name) if mat.shape[-1] > 1: if len(mat.shape) == 2: kwargs[name] = mat[:, -_out_of_sample:] else: kwargs[name] = mat[:, :, -_out_of_sample:] elif self.model.k_exog == 0 and exog is not None: warn('Exogenous array provided to predict, but additional data not' ' required. `exog` argument ignored.', ValueWarning) return super(DynamicFactorResults, self).get_prediction( start=start, end=end, dynamic=dynamic, index=index, exog=exog, **kwargs)
[docs] def summary(self, alpha=.05, start=None, separate_params=True): from statsmodels.iolib.summary import summary_params spec = self.specification # Create the model name model_name = [] if spec.k_factors > 0: if spec.factor_order > 0: model_type = ('DynamicFactor(factors=%d, order=%d)' % (spec.k_factors, spec.factor_order)) else: model_type = 'StaticFactor(factors=%d)' % spec.k_factors model_name.append(model_type) if spec.k_exog > 0: model_name.append('%d regressors' % spec.k_exog) else: model_name.append('SUR(%d regressors)' % spec.k_exog) if spec.error_order > 0: error_type = 'VAR' if spec.error_var else 'AR' model_name.append('%s(%d) errors' % (error_type, spec.error_order)) summary = super(DynamicFactorResults, self).summary( alpha=alpha, start=start, model_name=model_name, display_params=not separate_params ) if separate_params: indices = np.arange(len(self.params)) def make_table(self, mask, title, strip_end=True): res = (self, self.params[mask], self.bse[mask], self.zvalues[mask], self.pvalues[mask], self.conf_int(alpha)[mask]) param_names = [ '.'.join(name.split('.')[:-1]) if strip_end else name for name in np.array(self.data.param_names)[mask].tolist() ] return summary_params(res, yname=None, xname=param_names, alpha=alpha, use_t=False, title=title) k_endog = self.model.k_endog k_exog = self.model.k_exog k_factors = self.model.k_factors factor_order = self.model.factor_order _factor_order = self.model._factor_order _error_order = self.model._error_order # Add parameter tables for each endogenous variable loading_indices = indices[self.model._params_loadings] loading_masks = [] exog_indices = indices[self.model._params_exog] exog_masks = [] for i in range(k_endog): # 1. Factor loadings # Recall these are in the form: # 'loading.f1.y1', 'loading.f2.y1', 'loading.f1.y2', ... loading_mask = ( loading_indices[i * k_factors:(i + 1) * k_factors]) loading_masks.append(loading_mask) # 2. Exog # Recall these are in the form: # beta.x1.y1, beta.x2.y1, beta.x1.y2, ... exog_mask = exog_indices[i * k_exog:(i + 1) * k_exog] exog_masks.append(exog_mask) # Create the table mask = np.concatenate([loading_mask, exog_mask]) title = "Results for equation %s" % self.model.endog_names[i] table = make_table(self, mask, title) summary.tables.append(table) # Add parameter tables for each factor factor_indices = indices[self.model._params_factor_transition] factor_masks = [] if factor_order > 0: for i in range(k_factors): start = i * _factor_order factor_mask = factor_indices[start: start + _factor_order] factor_masks.append(factor_mask) # Create the table title = "Results for factor equation f%d" % (i+1) table = make_table(self, factor_mask, title) summary.tables.append(table) # Add parameter tables for error transitions error_masks = [] if spec.error_order > 0: error_indices = indices[self.model._params_error_transition] for i in range(k_endog): if spec.error_var: start = i * _error_order end = (i + 1) * _error_order else: start = i * spec.error_order end = (i + 1) * spec.error_order error_mask = error_indices[start:end] error_masks.append(error_mask) # Create the table title = ("Results for error equation e(%s)" % self.model.endog_names[i]) table = make_table(self, error_mask, title) summary.tables.append(table) # Error covariance terms error_cov_mask = indices[self.model._params_error_cov] table = make_table(self, error_cov_mask, "Error covariance matrix", strip_end=False) summary.tables.append(table) # Add a table for all other parameters masks = [] for m in (loading_masks, exog_masks, factor_masks, error_masks, [error_cov_mask]): m = np.array(m).flatten() if len(m) > 0: masks.append(m) masks = np.concatenate(masks) inverse_mask = np.array(list(set(indices).difference(set(masks)))) if len(inverse_mask) > 0: table = make_table(self, inverse_mask, "Other parameters", strip_end=False) summary.tables.append(table) return summary
summary.__doc__ = MLEResults.summary.__doc__
class DynamicFactorResultsWrapper(MLEResultsWrapper): _attrs = {} _wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs, _attrs) _methods = {} _wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods, _methods) wrap.populate_wrapper(DynamicFactorResultsWrapper, # noqa:E305 DynamicFactorResults)