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**Class: **ssm

State-space model simulation smoother

returns
simulated states (`X`

= simsmooth(`Mdl`

,`Y`

)`X`

) by applying a simulation smoother to
the time-invariant or time-varying state-space model (`Mdl`

)
and responses (`Y`

). That is, the software uses forward
filtering and back sampling to obtain one random path from the posterior
distribution of the states.

returns
simulated states with additional options specified by one or more `X`

= simsmooth(`Mdl`

,`Y`

,`Name,Value`

)`Name,Value`

pair
arguments.

The Kalman filter accommodates missing data by not updating filtered state estimates corresponding to missing observations. In other words, suppose there is a missing observation at period

*t*. Then, the state forecast for period*t*based on the previous*t*– 1 observations and filtered state for period*t*are equivalent.For increased speed in simulating states, the simulation smoother implements minimal dimensionality error checking. Therefore, for models with unknown parameter values, you should ensure that the dimensions of the data and the dimensions of the coefficient matrices are consistent.

[1] Durbin J., and S. J. Koopman. “A
Simple and Efficient Simulation Smoother for State Space Time Series
Analysis.” *Biometrika*. Vol 89., No.
3, 2002, pp. 603–615.

[2] Durbin J., and S. J. Koopman. *Time Series
Analysis by State Space Methods*. 2nd ed. Oxford: Oxford
University Press, 2012.