statsmodels.tsa.statespace.representation.Representation

class statsmodels.tsa.statespace.representation.Representation(k_endog, k_states, k_posdef=None, initial_variance=1000000.0, nobs=0, dtype=<class 'numpy.float64'>, design=None, obs_intercept=None, obs_cov=None, transition=None, state_intercept=None, selection=None, state_cov=None, statespace_classes=None, **kwargs)

State space representation of a time series process

Parameters:

• k_endog (array_like or integer) – The observed time-series process $y$ 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 ({'approximate_diffuse','stationary','known'}, optional) – Initialization method for the initial state.
• initial_state (array_like, optional) – If known initialization is used, the mean of the initial state’s distribution.
• initial_state_cov (array_like, optional) – If known initialization 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, $Z$. Default is set to zeros.
• obs_intercept (array_like, optional) – The intercept for the observation equation, $d$. Default is set to zeros.
• obs_cov (array_like, optional) – The covariance matrix for the observation equation $H$. Default is set to zeros.
• transition (array_like, optional) – The transition matrix, $T$. Default is set to zeros.
• state_intercept (array_like, optional) – The intercept for the transition equation, $c$. Default is set to zeros.
• selection (array_like, optional) – The selection matrix, $R$. Default is set to zeros.
• state_cov (array_like, optional) – The covariance matrix for the state equation $Q$. 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.

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

$$\begin{split}y_t & = Z_t \alpha_t + d_t + \varepsilon_t \\ \alpha_t & = T_t \alpha_{t-1} + c_t + R_t \eta_t \\\end{split}$$

where $y_t$ refers to the observation vector at time $t$, $\alpha_t$ refers to the (unobserved) state vector at time $t$, and where the irregular components are defined as

$$\begin{split}\varepsilon_t \sim N(0, H_t) \\ \eta_t \sim N(0, Q_t) \\\end{split}$$

The remaining variables ($Z_t, d_t, H_t, T_t, c_t, R_t, Q_t$) in the equations are matrices describing the process. Their variable names and dimensions are as follows

Z : design $(k\_endog \times k\_states \times nobs)$

d : obs_intercept $(k\_endog \times nobs)$

H : obs_cov $(k\_endog \times k\_endog \times nobs)$

T : transition $(k\_states \times k\_states \times nobs)$

c : state_intercept $(k\_states \times nobs)$

R : selection $(k\_states \times k\_posdef \times nobs)$

Q : state_cov $(k\_posdef \times k\_posdef \times nobs)$

In the case that one of the matrices is time-invariant (so that, for example, $Z_t = Z_{t+1} ~ \forall ~ t$), its last dimension may be of size $1$ rather than size nobs.

References

[*]	Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press.

Methods

bind(endog)	Bind data to the statespace representation
initialize_approximate_diffuse([variance])	Initialize the statespace model with approximate diffuse values.
initialize_known(initial_state, …)	Initialize the statespace model with known distribution for initial state.
initialize_stationary()	Initialize the statespace model as stationary.

Attributes

design	(array) Design matrix – $Z~(k\_endog \times k\_states \times nobs)$
dtype	(dtype) Datatype of currently active representation matrices
endog	(array) The observation vector, alias for obs.
obs	(array) Observation vector – $y~(k\_endog \times nobs)$
obs_cov	(array) Observation covariance matrix – $H~(k\_endog \times k\_endog \times nobs)$
obs_intercept	(array) Observation intercept – $d~(k\_endog \times nobs)$
prefix	(str) BLAS prefix of currently active representation matrices
selection	(array) Selection matrix – $R~(k\_states \times k\_posdef \times nobs)$
state_cov	(array) State covariance matrix – $Q~(k\_posdef \times k\_posdef \times nobs)$
state_intercept	(array) State intercept – $c~(k\_states \times nobs)$
time_invariant	(bool) Whether or not currently active representation matrices are time-invariant
transition	(array) Transition matrix – $T~(k\_states \times k\_states \times nobs)$