# statsmodels.tsa.statespace.representation.FrozenRepresentation¶

class statsmodels.tsa.statespace.representation.FrozenRepresentation(model)[source]

Frozen Statespace Model

Takes a snapshot of a Statespace model.

Parameters: model (Representation) – A Statespace representation
nobs

Number of observations.

Type: int
k_endog

The dimension of the observation series.

Type: int
k_states

The dimension of the unobserved state process.

Type: int
k_posdef

The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation.

Type: int
dtype

Datatype of representation matrices

Type: dtype
prefix

BLAS prefix of representation matrices

Type: str
shapes

A dictionary recording the shapes of each of the representation matrices as tuples.

Type: dictionary of name:tuple
endog

The observation vector.

Type: array
design

The design matrix, $$Z$$.

Type: array
obs_intercept

The intercept for the observation equation, $$d$$.

Type: array
obs_cov

The covariance matrix for the observation equation $$H$$.

Type: array
transition

The transition matrix, $$T$$.

Type: array
state_intercept

The intercept for the transition equation, $$c$$.

Type: array
selection

The selection matrix, $$R$$.

Type: array
state_cov

The covariance matrix for the state equation $$Q$$.

Type: array
missing

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.

Type: array of bool
nmissing

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.

Type: array of int
time_invariant

Whether or not the representation matrices are time-invariant

Type: bool
initialization

Kalman filter initialization method.

Type: Initialization object
initial_state

The state vector used to initialize the Kalamn filter.

Type: array_like
initial_state_cov

The state covariance matrix used to initialize the Kalamn filter.

Type: array_like

Methods