statsmodels.tsa.statespace.dynamic_factor.DynamicFactor

class statsmodels.tsa.statespace.dynamic_factor.DynamicFactor(endog, k_factors, factor_order, exog=None, error_order=0, error_var=False, error_cov_type='diagonal', enforce_stationarity=True, **kwargs)

Dynamic factor model

Parameters:

endog : array_like

The observed time-series process 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.

Notes

The dynamic factor model considered here is in the so-called static form, and is specified:

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 y_t is a k_endog x 1 vector and f_t is a k_factors x 1 vector.

x_t are optional exogenous vectors, shaped k_exog x 1.

\eta_t and \varepsilon_t are white noise error terms. In order to identify the factors, Var(\eta_t) = I. Denote Var(\varepsilon_t) \equiv \Sigma.

Options related to the unobserved factors:

• k_factors: this is the dimension of the vector 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 p, above. To have static factors, set factor_order = 0.

Options related to the observation error term u_t:

• error_order: the number of lags to include in the error evolution equation; corresponds to 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 \Sigma. If it is “dscalar”, then \Sigma = \sigma^2 I. If it is “diagonal”, then \Sigma = \text{diag}(\sigma_1^2, \dots, \sigma_n^2). If it is “unstructured”, then \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

[R60]

Lutkepohl, Helmut. 2007. New Introduction to Multiple Time Series Analysis. Berlin: Springer.

Attributes

Methods

filter(params, **kwargs)
fit([start_params, transformed, cov_type, ...])	Fits the model by maximum likelihood via Kalman filter.
from_formula(formula, data[, subset])	Not implemented for state space models
hessian(params, *args, **kwargs)	Hessian matrix of the likelihood function, evaluated at the given
impulse_responses(params[, steps, impulse, ...])	Impulse response function
information(params)	Fisher information matrix of model
initialize()	Initialize (possibly re-initialize) a Model instance.
initialize_approximate_diffuse([variance])
initialize_known(initial_state, ...)
initialize_statespace(**kwargs)	Initialize the state space representation
initialize_stationary()
loglike(params, *args, **kwargs)	Loglikelihood evaluation
loglikeobs(params[, transformed, complex_step])	Loglikelihood evaluation
observed_information_matrix(params[, ...])	Observed information matrix
opg_information_matrix(params[, ...])	Outer product of gradients information matrix
predict(params[, exog])	After a model has been fit predict returns the fitted values.
prepare_data()	Prepare data for use in the state space representation
score(params, *args, **kwargs)	Compute the score function at params.
score_obs(params[, method, transformed, ...])	Compute the score per observation, evaluated at params
set_conserve_memory([conserve_memory])	Set the memory conservation method
set_filter_method([filter_method])	Set the filtering method
set_inversion_method([inversion_method])	Set the inversion method
set_smoother_output([smoother_output])	Set the smoother output
set_stability_method([stability_method])	Set the numerical stability method
simulate(params, nsimulations[, ...])	Simulate a new time series following the state space model
smooth(params, **kwargs)
transform_jacobian(unconstrained[, ...])	Jacobian matrix for the parameter transformation function
transform_params(unconstrained)	Transform unconstrained parameters used by the optimizer to constrained
untransform_params(constrained)	Transform constrained parameters used in likelihood evaluation to unconstrained parameters used by the optimizer.
update(params[, transformed, complex_step])	Update the parameters of the model

Methods

filter(params, **kwargs)
fit([start_params, transformed, cov_type, ...])	Fits the model by maximum likelihood via Kalman filter.
from_formula(formula, data[, subset])	Not implemented for state space models
hessian(params, *args, **kwargs)	Hessian matrix of the likelihood function, evaluated at the given
impulse_responses(params[, steps, impulse, ...])	Impulse response function
information(params)	Fisher information matrix of model
initialize()	Initialize (possibly re-initialize) a Model instance.
initialize_approximate_diffuse([variance])
initialize_known(initial_state, ...)
initialize_statespace(**kwargs)	Initialize the state space representation
initialize_stationary()
loglike(params, *args, **kwargs)	Loglikelihood evaluation
loglikeobs(params[, transformed, complex_step])	Loglikelihood evaluation
observed_information_matrix(params[, ...])	Observed information matrix
opg_information_matrix(params[, ...])	Outer product of gradients information matrix
predict(params[, exog])	After a model has been fit predict returns the fitted values.
prepare_data()	Prepare data for use in the state space representation
score(params, *args, **kwargs)	Compute the score function at params.
score_obs(params[, method, transformed, ...])	Compute the score per observation, evaluated at params
set_conserve_memory([conserve_memory])	Set the memory conservation method
set_filter_method([filter_method])	Set the filtering method
set_inversion_method([inversion_method])	Set the inversion method
set_smoother_output([smoother_output])	Set the smoother output
set_stability_method([stability_method])	Set the numerical stability method
simulate(params, nsimulations[, ...])	Simulate a new time series following the state space model
smooth(params, **kwargs)
transform_jacobian(unconstrained[, ...])	Jacobian matrix for the parameter transformation function
transform_params(unconstrained)	Transform unconstrained parameters used by the optimizer to constrained
untransform_params(constrained)	Transform constrained parameters used in likelihood evaluation to unconstrained parameters used by the optimizer.
update(params[, transformed, complex_step])	Update the parameters of the model

Attributes

endog_names	Names of endogenous variables
exog_names
initial_variance
initialization
loglikelihood_burn
param_names
start_params
tolerance