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Create Markov-switching dynamic regression model

The `msVAR`

function returns an
`msVAR`

object that specifies the functional form of a Markov-switching dynamic regression
model for the univariate or multivariate response process
*y*_{t}. The `msVAR`

object also
stores the parameter values of the model.

An `msVAR`

object has two key components: the switching mechanism
among states, represented by a discrete-time Markov chain (`dtmc`

object); and the
state-specific submodels, either autoregressive (ARX) or vector autoregression (VARX) models
(`arima`

or `varm`

objects), which can contain exogenous regression
components. The components completely specify the model structure. The Markov chain transition
matrix and submodel parameters, such as the AR coefficients and innovation-distribution
variance, are unknown and estimable unless you specify their values.

To estimate a model containing unknown parameter values, pass the model and data to
`estimate`

. To work
with an estimated or fully specified `msVAR`

object, pass it to an object function.

Alternatively, to create a threshold-switching dynamic regression model, which has a
switching mechanism governed by threshold transitions and observations of a threshold
variable, see `threshold`

and `tsVAR`

.

optionally sets the SeriesNames property, which
associates the names `Mdl`

= msVAR(`mc`

,`mdl`

,`'SeriesNames'`

,seriesNames)`seriesNames`

to the time series of the
model.

`estimate` | Fit Markov-switching dynamic regression model to data |

`filter` | Filtered inference of operative latent states in Markov-switching dynamic regression data |

`forecast` | Forecast sample paths from Markov-switching dynamic regression model |

`simulate` | Simulate sample paths of Markov-switching dynamic regression model |

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

`summarize` | Summarize Markov-switching dynamic regression model estimation results |

[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, James D. *Time Series Analysis*. Princeton, NJ: Princeton University Press, 1994.

[5]
Krolzig, H.-M. *Markov-Switching Vector Autoregressions*. Berlin: Springer, 1997.