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discrete-time markov model containing switching state and dynamic regression submodels

a markov-switching dynamic regression model describes the dynamic behavior of time series variables in the presence of structural breaks or regime changes. a discrete-time markov chain () represents the discrete state space of the regimes and specifies the probabilistic switching mechanism among the regimes. a collection of dynamic regression (arx or varx) submodels ( or ) describes the dynamic behavior of the time series within the regimes.

to create a markov-switching dynamic regression model, see .

functions

create markov-switching dynamic regression model
create discrete-time markov chain
create univariate autoregressive integrated moving average (arima) model
create vector autoregression (var) model
fit markov-switching dynamic regression model to data
summarize markov-switching dynamic regression model estimation results
filtered inference of operative latent states in markov-switching dynamic regression data
smoothed inference of operative latent states in markov-switching dynamic regression data
simulate sample paths of markov-switching dynamic regression model
forecast sample paths from markov-switching dynamic regression model

topics

create model


  • learn requirements for creating a markov-switching dynamic regression model by using msvar.

  • create a fully or partially specified univariate markov-switching dynamic regression model by using msvar.

  • adjust the specifications of a created markov-switching dynamic regression model.

  • create a fully or partially specified markov-switching dynamic regression model for a multivariate response process by using msvar.

fit model to data


  • fit a univariate markov-switching dynamic regression model of the us unemployment rate to time series data and simulate and forecast unemployment rate paths from the estimated model.

generate monte carlo simulations


  • generate random response and state paths from a two-state markov-switching dynamic regression model.

  • generate random response and state paths from a three-state markov-switching dynamic regression model.

  • characterize the distribution of a multivariate response series, modeled by a markov-switching dynamic regression model, by summarizing the draws of a monte carlo simulation.
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