# statsmodels.tsa.statespace.cfa_simulation_smoother.CFASimulationSmoother.simulate¶

CFASimulationSmoother.simulate(variates=None, update_posterior=True)[source]

Perform simulation smoothing (via Cholesky factor algorithm)

Does not return anything, but populates the object’s simulated_state attribute, and also makes available the attributes posterior_mean, posterior_cov, and posterior_cov_inv_chol_sparse.

Parameters
variatesarray_like, optional

Random variates, distributed standard Normal. Usually only specified if results are to be replicated (e.g. to enforce a seed) or for testing. If not specified, random variates are drawn. Must be shaped (nobs, k_states).

Notes

The first step in simulating from the joint posterior of the state vector conditional on the data is to compute the two relevant moments of the joint posterior distribution:

$\alpha \mid Y_n \sim N(\hat \alpha, Var(\alpha \mid Y_n))$

Let $$L L' = Var(\alpha \mid Y_n)^{-1}$$. Then simulation proceeds according to the following steps:

1. Draw $$u \sim N(0, I)$$

2. Compute $$x = \hat \alpha + (L')^{-1} u$$

And then $$x$$ is a draw from the joint posterior of the states. The output of the function is as follows:

• The simulated draw $$x$$ is held in the simulated_state attribute.

• The posterior mean $$\hat \alpha$$ is held in the posterior_mean attribute.

• The (lower triangular) Cholesky factor of the inverse posterior covariance matrix, $$L$$, is held in sparse diagonal banded storage in the posterior_cov_inv_chol attribute.

• The posterior covariance matrix $$Var(\alpha \mid Y_n)$$ can be computed on demand by accessing the posterior_cov property. Note that this matrix can be extremely large, so care must be taken when accessing this property. In most cases, it will be preferred to make use of the posterior_cov_inv_chol attribute rather than the posterior_cov attribute.