Smoothed inference of operative latent states in Markov-switching dynamic regression data

uses additional
options specified by one or more name-value pair arguments. For example, `SS`

= smooth(`Mdl`

,`Y`

,`Name,Value`

)`'Y0',Y0`

initializes the dynamic component of each submodel by using the presample response data `Y0`

.

`smooth`

refines current estimates of the state distribution that `filter`

produces by iterating backward from the full sample history `Y`

.

[1]
Chauvet, M., and J. D. Hamilton. "Dating Business Cycle Turning Points." In *Nonlinear Analysis of Business Cycles (Contributions to Economic Analysis, Volume 276)*. (C. Milas, P. Rothman, and D. van Dijk, eds.). Amsterdam: Emerald Group Publishing Limited, 2006.

[2]
Hamilton, J. D. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle." *Econometrica*. Vol. 57, 1989, pp. 357–384.

[3]
Hamilton, J. D. "Analysis of Time Series Subject to Changes in Regime." *Journal of Econometrics*. Vol. 45, 1990, pp. 39–70.

[4]
Hamilton, J. D. *Time Series Analysis*. Princeton, NJ: Princeton University Press, 1994.

[5]
Kim, C.-J. "Dynamic Linear Models with Markov Switching." *Journal of Econometrics*. Vol. 60, 1994, pp. 1–22.