mskf-package: Markov Switching Kalman Filter
In mskf: Markov Switching Kalman Filter

Description Details Author(s) References See Also Examples

Fitting a regime switching time series models using mixed Kalman and Hamilton filter.

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The mskf package is used for fitting state space models to multivariate time series in which the dynamics changes according to switches between unobserved regimes. The package supports multiple state indicator (endogenous) timeseries, exogenous timeseries, path structure specification, equality and box-constraints, and handles missing data.

The main functions for fitting a model to a multivariate time series (mts) object are:

mskf.model.skeleton which for an mts object returns a model skeleton that contains the necessary model parameter matrices for the indicated dimension of the state space process, the number of regimes, and the (optional) exogenous time series

mskf.model which turns the model skeleton into a fully specified model by providing influence path structures, fixed values, starting values and box constraints (upper and lower bounds on estimated parameters).

mskf does the actual fitting of the model.

plot draws a graphical representation of the state space model under the different regimes

summary gives model fit summary in terms of likelihood, goodness of fit (AIC), parameter estimates and confidence intervals

simulate simulates data from a model

Ellen L. Hamaker [aut], Raoul P. P. P. Grasman [aut, cre]

Maintainer: Raoul P. P. P. Grasman <rgrasman@uva.nl>

Kim, C.J. (1994). Dynamic linear models with Markov-switching. Journal of Econometrics, 60, 1???22.

Kim, C.-J., and Nelson, C. R. (1999). State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. Cambridge, MIT press.

Hamaker, E. L. & Grasman, R. P. P. P. (2012) Regime Switching State-Space Models Applied To Psychological Processes: Handling Missing Data And Making Inferences. Psychometrika, 77:2, 400???422. URL http://doi.org/10.1007/S11336-012-9254-8

mskf