Source code for statsmodels.tsa.statespace.representation

"""
State Space Representation

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

import numpy as np
from .tools import (
    find_best_blas_type, validate_matrix_shape, validate_vector_shape
)
from .initialization import Initialization
from . import tools


class OptionWrapper(object):
    def __init__(self, mask_attribute, mask_value):
        # Name of the class-level bitmask attribute
        self.mask_attribute = mask_attribute
        # Value of this option
        self.mask_value = mask_value

    def __get__(self, obj, objtype):
        # Return True / False based on whether the bit is set in the bitmask
        return bool(getattr(obj, self.mask_attribute, 0) & self.mask_value)

    def __set__(self, obj, value):
        mask_attribute_value = getattr(obj, self.mask_attribute, 0)
        if bool(value):
            value = mask_attribute_value | self.mask_value
        else:
            value = mask_attribute_value & ~self.mask_value
        setattr(obj, self.mask_attribute, value)


class MatrixWrapper(object):
    def __init__(self, name, attribute):
        self.name = name
        self.attribute = attribute
        self._attribute = '_' + attribute

    def __get__(self, obj, objtype):
        matrix = getattr(obj, self._attribute, None)
        # # Remove last dimension if the array is not actually time-varying
        # if matrix is not None and matrix.shape[-1] == 1:
        #     return np.squeeze(matrix, -1)
        return matrix

    def __set__(self, obj, value):
        value = np.asarray(value, order="F")
        shape = obj.shapes[self.attribute]

        if len(shape) == 3:
            value = self._set_matrix(obj, value, shape)
        else:
            value = self._set_vector(obj, value, shape)

        setattr(obj, self._attribute, value)
        obj.shapes[self.attribute] = value.shape

    def _set_matrix(self, obj, value, shape):
        # Expand 1-dimensional array if possible
        if (value.ndim == 1 and shape[0] == 1 and
                value.shape[0] == shape[1]):
            value = value[None, :]

        # Enforce that the matrix is appropriate size
        validate_matrix_shape(
            self.name, value.shape, shape[0], shape[1], obj.nobs
        )

        # Expand time-invariant matrix
        if value.ndim == 2:
            value = np.array(value[:, :, None], order="F")

        return value

    def _set_vector(self, obj, value, shape):
        # Enforce that the vector has appropriate length
        validate_vector_shape(
            self.name, value.shape, shape[0], obj.nobs
        )

        # Expand the time-invariant vector
        if value.ndim == 1:
            value = np.array(value[:, None], order="F")

        return value


[docs]class Representation(object): r""" State space representation of a time series process Parameters ---------- k_endog : array_like or integer The observed time-series process :math:`y` if array like or the number of variables in the process if an integer. k_states : int The dimension of the unobserved state process. k_posdef : int, optional The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation. Must be less than or equal to `k_states`. Default is `k_states`. initial_variance : float, optional Initial variance used when approximate diffuse initialization is specified. Default is 1e6. initialization : Initialization object or string, optional Initialization method for the initial state. If a string, must be one of {'diffuse', 'approximate_diffuse', 'stationary', 'known'}. initial_state : array_like, optional If `initialization='known'` is used, the mean of the initial state's distribution. initial_state_cov : array_like, optional If `initialization='known'` is used, the covariance matrix of the initial state's distribution. nobs : integer, optional If an endogenous vector is not given (i.e. `k_endog` is an integer), the number of observations can optionally be specified. If not specified, they will be set to zero until data is bound to the model. dtype : np.dtype, optional If an endogenous vector is not given (i.e. `k_endog` is an integer), the default datatype of the state space matrices can optionally be specified. Default is `np.float64`. design : array_like, optional The design matrix, :math:`Z`. Default is set to zeros. obs_intercept : array_like, optional The intercept for the observation equation, :math:`d`. Default is set to zeros. obs_cov : array_like, optional The covariance matrix for the observation equation :math:`H`. Default is set to zeros. transition : array_like, optional The transition matrix, :math:`T`. Default is set to zeros. state_intercept : array_like, optional The intercept for the transition equation, :math:`c`. Default is set to zeros. selection : array_like, optional The selection matrix, :math:`R`. Default is set to zeros. state_cov : array_like, optional The covariance matrix for the state equation :math:`Q`. Default is set to zeros. **kwargs Additional keyword arguments. Not used directly. It is present to improve compatibility with subclasses, so that they can use `**kwargs` to specify any default state space matrices (e.g. `design`) without having to clean out any other keyword arguments they might have been passed. Attributes ---------- nobs : int The number of observations. k_endog : int The dimension of the observation series. k_states : int The dimension of the unobserved state process. k_posdef : int The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation. shapes : dictionary of name:tuple A dictionary recording the initial shapes of each of the representation matrices as tuples. initialization : str Kalman filter initialization method. Default is unset. initial_variance : float Initial variance for approximate diffuse initialization. Default is 1e6. Notes ----- A general state space model is of the form .. math:: y_t & = Z_t \alpha_t + d_t + \varepsilon_t \\ \alpha_t & = T_t \alpha_{t-1} + c_t + R_t \eta_t \\ where :math:`y_t` refers to the observation vector at time :math:`t`, :math:`\alpha_t` refers to the (unobserved) state vector at time :math:`t`, and where the irregular components are defined as .. math:: \varepsilon_t \sim N(0, H_t) \\ \eta_t \sim N(0, Q_t) \\ The remaining variables (:math:`Z_t, d_t, H_t, T_t, c_t, R_t, Q_t`) in the equations are matrices describing the process. Their variable names and dimensions are as follows Z : `design` :math:`(k\_endog \times k\_states \times nobs)` d : `obs_intercept` :math:`(k\_endog \times nobs)` H : `obs_cov` :math:`(k\_endog \times k\_endog \times nobs)` T : `transition` :math:`(k\_states \times k\_states \times nobs)` c : `state_intercept` :math:`(k\_states \times nobs)` R : `selection` :math:`(k\_states \times k\_posdef \times nobs)` Q : `state_cov` :math:`(k\_posdef \times k\_posdef \times nobs)` In the case that one of the matrices is time-invariant (so that, for example, :math:`Z_t = Z_{t+1} ~ \forall ~ t`), its last dimension may be of size :math:`1` rather than size `nobs`. References ---------- .. [*] Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press. """ endog = None r""" (array) The observation vector, alias for `obs`. """ design = MatrixWrapper('design', 'design') r""" (array) Design matrix: :math:`Z~(k\_endog \times k\_states \times nobs)` """ obs_intercept = MatrixWrapper('observation intercept', 'obs_intercept') r""" (array) Observation intercept: :math:`d~(k\_endog \times nobs)` """ obs_cov = MatrixWrapper('observation covariance matrix', 'obs_cov') r""" (array) Observation covariance matrix: :math:`H~(k\_endog \times k\_endog \times nobs)` """ transition = MatrixWrapper('transition', 'transition') r""" (array) Transition matrix: :math:`T~(k\_states \times k\_states \times nobs)` """ state_intercept = MatrixWrapper('state intercept', 'state_intercept') r""" (array) State intercept: :math:`c~(k\_states \times nobs)` """ selection = MatrixWrapper('selection', 'selection') r""" (array) Selection matrix: :math:`R~(k\_states \times k\_posdef \times nobs)` """ state_cov = MatrixWrapper('state covariance matrix', 'state_cov') r""" (array) State covariance matrix: :math:`Q~(k\_posdef \times k\_posdef \times nobs)` """ def __init__(self, k_endog, k_states, k_posdef=None, initial_variance=1e6, nobs=0, dtype=np.float64, design=None, obs_intercept=None, obs_cov=None, transition=None, state_intercept=None, selection=None, state_cov=None, statespace_classes=None, **kwargs): self.shapes = {} # Check if k_endog is actually the endog array endog = None if isinstance(k_endog, np.ndarray): endog = k_endog # If so, assume that it is either column-ordered and in wide format # or row-ordered and in long format if (endog.flags['C_CONTIGUOUS'] and (endog.shape[0] > 1 or nobs == 1)): endog = endog.T k_endog = endog.shape[0] # Endogenous array, dimensions, dtype self.k_endog = k_endog if k_endog < 1: raise ValueError('Number of endogenous variables in statespace' ' model must be a positive number.') self.nobs = nobs # Get dimensions from transition equation if k_states < 1: raise ValueError('Number of states in statespace model must be a' ' positive number.') self.k_states = k_states self.k_posdef = k_posdef if k_posdef is not None else k_states # Make sure k_posdef <= k_states # TODO: we could technically allow k_posdef > k_states, but the Cython # code needs to be more thoroughly checked to avoid seg faults. if self.k_posdef > self.k_states: raise ValueError('Dimension of state innovation `k_posdef` cannot' ' be larger than the dimension of the state.') # Bind endog, if it was given if endog is not None: self.bind(endog) # Record the shapes of all of our matrices # Note: these are time-invariant shapes; in practice the last dimension # may also be `self.nobs` for any or all of these. self.shapes = { 'obs': (self.k_endog, self.nobs), 'design': (self.k_endog, self.k_states, 1), 'obs_intercept': (self.k_endog, 1), 'obs_cov': (self.k_endog, self.k_endog, 1), 'transition': (self.k_states, self.k_states, 1), 'state_intercept': (self.k_states, 1), 'selection': (self.k_states, self.k_posdef, 1), 'state_cov': (self.k_posdef, self.k_posdef, 1), } # Representation matrices # These matrices are only used in the Python object as containers, # which will be copied to the appropriate _statespace object if a # filter is called. scope = locals() for name, shape in self.shapes.items(): if name == 'obs': continue # Create the initial storage array for each matrix setattr(self, '_' + name, np.zeros(shape, dtype=dtype, order="F")) # If we were given an initial value for the matrix, set it # (notice it is being set via the descriptor) if scope[name] is not None: setattr(self, name, scope[name]) # Options self.initial_variance = initial_variance self.prefix_statespace_map = (statespace_classes if statespace_classes is not None else tools.prefix_statespace_map.copy()) # State-space initialization data self.initialization = kwargs.get('initialization', None) basic_inits = ['diffuse', 'approximate_diffuse', 'stationary'] if self.initialization in basic_inits: self.initialize(self.initialization) elif self.initialization == 'known': if 'constant' in kwargs: constant = kwargs['constant'] elif 'initial_state' in kwargs: # TODO deprecation warning constant = kwargs['initial_state'] else: raise ValueError('Initial state must be provided when "known"' ' is the specified initialization method.') if 'stationary_cov' in kwargs: stationary_cov = kwargs['stationary_cov'] elif 'initial_state_cov' in kwargs: # TODO deprecation warning stationary_cov = kwargs['initial_state_cov'] else: raise ValueError('Initial state covariance matrix must be' ' provided when "known" is the specified' ' initialization method.') self.initialize('known', constant=constant, stationary_cov=stationary_cov) elif (not isinstance(self.initialization, Initialization) and self.initialization is not None): raise ValueError("Invalid state space initialization method.") # Matrix representations storage self._representations = {} # Setup the underlying statespace object storage self._statespaces = {} # Caches self._time_invariant = None def __getitem__(self, key): _type = type(key) # If only a string is given then we must be getting an entire matrix if _type is str: if key not in self.shapes: raise IndexError('"%s" is an invalid state space matrix name' % key) matrix = getattr(self, '_' + key) # See note on time-varying arrays, below if matrix.shape[-1] == 1: return matrix[(slice(None),)*(matrix.ndim-1) + (0,)] else: return matrix # Otherwise if we have a tuple, we want a slice of a matrix elif _type is tuple: name, slice_ = key[0], key[1:] if name not in self.shapes: raise IndexError('"%s" is an invalid state space matrix name' % name) matrix = getattr(self, '_' + name) # Since the model can support time-varying arrays, but often we # will instead have time-invariant arrays, we want to allow setting # a matrix slice like mod['transition',0,:] even though technically # it should be mod['transition',0,:,0]. Thus if the array in # question is time-invariant but the last slice was excluded, # add it in as a zero. if matrix.shape[-1] == 1 and len(slice_) <= matrix.ndim-1: slice_ = slice_ + (0,) return matrix[slice_] # Otherwise, we have only a single slice index, but it is not a string else: raise IndexError('First index must the name of a valid state space' ' matrix.') def __setitem__(self, key, value): _type = type(key) # If only a string is given then we must be setting an entire matrix if _type is str: if key not in self.shapes: raise IndexError('"%s" is an invalid state space matrix name' % key) setattr(self, key, value) # If it's a tuple (with a string as the first element) then we must be # setting a slice of a matrix elif _type is tuple: name, slice_ = key[0], key[1:] if name not in self.shapes: raise IndexError('"%s" is an invalid state space matrix name' % key[0]) # Change the dtype of the corresponding matrix dtype = np.array(value).dtype matrix = getattr(self, '_' + name) valid_types = ['f', 'd', 'F', 'D'] if not matrix.dtype == dtype and dtype.char in valid_types: matrix = getattr(self, '_' + name).real.astype(dtype) # Since the model can support time-varying arrays, but often we # will instead have time-invariant arrays, we want to allow setting # a matrix slice like mod['transition',0,:] even though technically # it should be mod['transition',0,:,0]. Thus if the array in # question is time-invariant but the last slice was excluded, # add it in as a zero. if matrix.shape[-1] == 1 and len(slice_) == matrix.ndim-1: slice_ = slice_ + (0,) # Set the new value matrix[slice_] = value setattr(self, name, matrix) # Otherwise we got a single non-string key, (e.g. mod[:]), which is # invalid else: raise IndexError('First index must the name of a valid state space' ' matrix.') @property def prefix(self): """ (str) BLAS prefix of currently active representation matrices """ arrays = ( self._design, self._obs_intercept, self._obs_cov, self._transition, self._state_intercept, self._selection, self._state_cov ) if self.endog is not None: arrays = (self.endog,) + arrays return find_best_blas_type(arrays)[0] @property def dtype(self): """ (dtype) Datatype of currently active representation matrices """ return tools.prefix_dtype_map[self.prefix] @property def time_invariant(self): """ (bool) Whether or not currently active representation matrices are time-invariant """ if self._time_invariant is None: return ( self._design.shape[2] == self._obs_intercept.shape[1] == self._obs_cov.shape[2] == self._transition.shape[2] == self._state_intercept.shape[1] == self._selection.shape[2] == self._state_cov.shape[2] ) else: return self._time_invariant @property def _statespace(self): prefix = self.prefix if prefix in self._statespaces: return self._statespaces[prefix] return None @property def obs(self): r""" (array) Observation vector: :math:`y~(k\_endog \times nobs)` """ return self.endog
[docs] def bind(self, endog): """ Bind data to the statespace representation Parameters ---------- endog : array Endogenous data to bind to the model. Must be column-ordered ndarray with shape (`k_endog`, `nobs`) or row-ordered ndarray with shape (`nobs`, `k_endog`). Notes ----- The strict requirements arise because the underlying statespace and Kalman filtering classes require Fortran-ordered arrays in the wide format (shaped (`k_endog`, `nobs`)), and this structure is setup to prevent copying arrays in memory. By default, numpy arrays are row (C)-ordered and most time series are represented in the long format (with time on the 0-th axis). In this case, no copying or re-ordering needs to be performed, instead the array can simply be transposed to get it in the right order and shape. Although this class (Representation) has stringent `bind` requirements, it is assumed that it will rarely be used directly. """ if not isinstance(endog, np.ndarray): raise ValueError("Invalid endogenous array; must be an ndarray.") # Make sure we have a 2-dimensional array # Note: reshaping a 1-dim array into a 2-dim array by changing the # shape tuple always results in a row (C)-ordered array, so it # must be shaped (nobs, k_endog) if endog.ndim == 1: # In the case of nobs x 0 arrays if self.k_endog == 1: endog.shape = (endog.shape[0], 1) # In the case of k_endog x 0 arrays else: endog.shape = (1, endog.shape[0]) if not endog.ndim == 2: raise ValueError('Invalid endogenous array provided; must be' ' 2-dimensional.') # Check for valid column-ordered arrays if endog.flags['F_CONTIGUOUS'] and endog.shape[0] == self.k_endog: pass # Check for valid row-ordered arrays, and transpose them to be the # correct column-ordered array elif endog.flags['C_CONTIGUOUS'] and endog.shape[1] == self.k_endog: endog = endog.T # Invalid column-ordered arrays elif endog.flags['F_CONTIGUOUS']: raise ValueError('Invalid endogenous array; column-ordered' ' arrays must have first axis shape of' ' `k_endog`.') # Invalid row-ordered arrays elif endog.flags['C_CONTIGUOUS']: raise ValueError('Invalid endogenous array; row-ordered' ' arrays must have last axis shape of' ' `k_endog`.') # Non-contiguous arrays else: raise ValueError('Invalid endogenous array; must be ordered in' ' contiguous memory.') # In some corner cases (e.g. np.array(1., ndmin=2) with numpy < 1.8) # we may still have a non-fortran contiguous array, so double-check # that now if not endog.flags['F_CONTIGUOUS']: endog = np.asfortranarray(endog) # Set a flag for complex data self._complex_endog = np.iscomplexobj(endog) # Set the data self.endog = endog self.nobs = self.endog.shape[1] # Reset shapes if hasattr(self, 'shapes'): self.shapes['obs'] = self.endog.shape
[docs] def initialize(self, initialization, approximate_diffuse_variance=None, constant=None, stationary_cov=None): # Create an Initialization object if necessary if initialization == 'known': initialization = Initialization(self.k_states, 'known', constant=constant, stationary_cov=stationary_cov) elif initialization == 'approximate_diffuse': if approximate_diffuse_variance is None: approximate_diffuse_variance = self.initial_variance initialization = Initialization( self.k_states, 'approximate_diffuse', approximate_diffuse_variance=approximate_diffuse_variance) elif initialization == 'stationary': initialization = Initialization(self.k_states, 'stationary') elif initialization == 'diffuse': initialization = Initialization(self.k_states, 'diffuse') # We must have an initialization object at this point if not isinstance(initialization, Initialization): raise ValueError("Invalid state space initialization method.") self.initialization = initialization
[docs] def initialize_known(self, constant, stationary_cov): """ Initialize the statespace model with known distribution for initial state. These values are assumed to be known with certainty or else filled with parameters during, for example, maximum likelihood estimation. Parameters ---------- constant : array_like Known mean of the initial state vector. stationary_cov : array_like Known covariance matrix of the initial state vector. """ constant = np.asarray(constant, order="F") stationary_cov = np.asarray(stationary_cov, order="F") if not constant.shape == (self.k_states,): raise ValueError('Invalid dimensions for constant state vector.' ' Requires shape (%d,), got %s' % (self.k_states, str(constant.shape))) if not stationary_cov.shape == (self.k_states, self.k_states): raise ValueError('Invalid dimensions for stationary covariance' ' matrix. Requires shape (%d,%d), got %s' % (self.k_states, self.k_states, str(stationary_cov.shape))) self.initialize('known', constant=constant, stationary_cov=stationary_cov)
[docs] def initialize_approximate_diffuse(self, variance=None): """ Initialize the statespace model with approximate diffuse values. Rather than following the exact diffuse treatment (which is developed for the case that the variance becomes infinitely large), this assigns an arbitrary large number for the variance. Parameters ---------- variance : float, optional The variance for approximating diffuse initial conditions. Default is 1e6. """ if variance is None: variance = self.initial_variance self.initialize('approximate_diffuse', approximate_diffuse_variance=variance)
[docs] def initialize_stationary(self): """ Initialize the statespace model as stationary. """ self.initialize('stationary')
[docs] def initialize_diffuse(self): """ Initialize the statespace model as stationary. """ self.initialize('diffuse')
def _initialize_representation(self, prefix=None): if prefix is None: prefix = self.prefix dtype = tools.prefix_dtype_map[prefix] # If the dtype-specific representation matrices do not exist, create # them if prefix not in self._representations: # Copy the statespace representation matrices self._representations[prefix] = {} for matrix in self.shapes.keys(): if matrix == 'obs': self._representations[prefix][matrix] = ( self.obs.astype(dtype) ) else: # Note: this always makes a copy self._representations[prefix][matrix] = ( getattr(self, '_' + matrix).astype(dtype) ) # If they do exist, update them else: for matrix in self.shapes.keys(): existing = self._representations[prefix][matrix] if matrix == 'obs': # existing[:] = self.obs.astype(dtype) pass else: new = getattr(self, '_' + matrix).astype(dtype) if existing.shape == new.shape: existing[:] = new[:] else: self._representations[prefix][matrix] = new # Determine if we need to (re-)create the _statespace models # (if time-varying matrices changed) if prefix in self._statespaces: ss = self._statespaces[prefix] create = ( not ss.obs.shape[1] == self.endog.shape[1] or not ss.design.shape[2] == self.design.shape[2] or not ss.obs_intercept.shape[1] == self.obs_intercept.shape[1] or not ss.obs_cov.shape[2] == self.obs_cov.shape[2] or not ss.transition.shape[2] == self.transition.shape[2] or not (ss.state_intercept.shape[1] == self.state_intercept.shape[1]) or not ss.selection.shape[2] == self.selection.shape[2] or not ss.state_cov.shape[2] == self.state_cov.shape[2] ) else: create = True # (re-)create if necessary if create: if prefix in self._statespaces: del self._statespaces[prefix] # Setup the base statespace object cls = self.prefix_statespace_map[prefix] self._statespaces[prefix] = cls( self._representations[prefix]['obs'], self._representations[prefix]['design'], self._representations[prefix]['obs_intercept'], self._representations[prefix]['obs_cov'], self._representations[prefix]['transition'], self._representations[prefix]['state_intercept'], self._representations[prefix]['selection'], self._representations[prefix]['state_cov'] ) return prefix, dtype, create def _initialize_state(self, prefix=None, complex_step=False): # TODO once the transition to using the Initialization objects is # complete, this should be moved entirely to the _{{prefix}}Statespace # object. if prefix is None: prefix = self.prefix # (Re-)initialize the statespace model if isinstance(self.initialization, Initialization): if not self.initialization.initialized: raise RuntimeError('Initialization is incomplete.') self._statespaces[prefix].initialize(self.initialization, complex_step=complex_step) else: raise RuntimeError('Statespace model not initialized.')
[docs]class FrozenRepresentation(object): """ Frozen Statespace Model Takes a snapshot of a Statespace model. Parameters ---------- model : Representation A Statespace representation Attributes ---------- nobs : int Number of observations. k_endog : int The dimension of the observation series. k_states : int The dimension of the unobserved state process. k_posdef : int The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation. dtype : dtype Datatype of representation matrices prefix : str BLAS prefix of representation matrices shapes : dictionary of name:tuple A dictionary recording the shapes of each of the representation matrices as tuples. endog : array The observation vector. design : array The design matrix, :math:`Z`. obs_intercept : array The intercept for the observation equation, :math:`d`. obs_cov : array The covariance matrix for the observation equation :math:`H`. transition : array The transition matrix, :math:`T`. state_intercept : array The intercept for the transition equation, :math:`c`. selection : array The selection matrix, :math:`R`. state_cov : array The covariance matrix for the state equation :math:`Q`. missing : array of bool An array of the same size as `endog`, filled with boolean values that are True if the corresponding entry in `endog` is NaN and False otherwise. nmissing : array of int An array of size `nobs`, where the ith entry is the number (between 0 and `k_endog`) of NaNs in the ith row of the `endog` array. time_invariant : bool Whether or not the representation matrices are time-invariant initialization : Initialization object Kalman filter initialization method. initial_state : array_like The state vector used to initialize the Kalamn filter. initial_state_cov : array_like The state covariance matrix used to initialize the Kalamn filter. """ _model_attributes = [ 'model', 'prefix', 'dtype', 'nobs', 'k_endog', 'k_states', 'k_posdef', 'time_invariant', 'endog', 'design', 'obs_intercept', 'obs_cov', 'transition', 'state_intercept', 'selection', 'state_cov', 'missing', 'nmissing', 'shapes', 'initialization', 'initial_state', 'initial_state_cov', 'initial_variance' ] _attributes = _model_attributes def __init__(self, model): # Initialize all attributes to None for name in self._attributes: setattr(self, name, None) # Update the representation attributes self.update_representation(model)
[docs] def update_representation(self, model): # Model self.model = model # Data type self.prefix = model.prefix self.dtype = model.dtype # Copy the model dimensions self.nobs = model.nobs self.k_endog = model.k_endog self.k_states = model.k_states self.k_posdef = model.k_posdef self.time_invariant = model.time_invariant # Save the state space representation at the time self.endog = model.endog self.design = model._design.copy() self.obs_intercept = model._obs_intercept.copy() self.obs_cov = model._obs_cov.copy() self.transition = model._transition.copy() self.state_intercept = model._state_intercept.copy() self.selection = model._selection.copy() self.state_cov = model._state_cov.copy() self.missing = np.array(model._statespaces[self.prefix].missing, copy=True) self.nmissing = np.array(model._statespaces[self.prefix].nmissing, copy=True) # Save the final shapes of the matrices self.shapes = dict(model.shapes) for name in self.shapes.keys(): if name == 'obs': continue self.shapes[name] = getattr(self, name).shape self.shapes['obs'] = self.endog.shape # Save the state space initialization self.initialization = model.initialization if model.initialization is not None: model._initialize_state() self.initial_state = np.array( model._statespaces[self.prefix].initial_state, copy=True) self.initial_state_cov = np.array( model._statespaces[self.prefix].initial_state_cov, copy=True) self.initial_diffuse_state_cov = np.array( model._statespaces[self.prefix].initial_diffuse_state_cov, copy=True)