Main Content

Markov-Switching Dynamic Regression Model

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 (dtmc) 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 (arima or varm) describes the dynamic behavior of the time series within the regimes.

To create a Markov-switching dynamic regression model, see msVAR.

Functions

expand all

msVARCreate Markov-switching dynamic regression model
dtmcCreate discrete-time Markov chain
arimaCreate univariate autoregressive integrated moving average (ARIMA) model
varmCreate vector autoregression (VAR) model
estimateFit Markov-switching dynamic regression model to data
summarizeSummarize Markov-switching dynamic regression model estimation results
filterFiltered inference of operative latent states in Markov-switching dynamic regression data
smoothSmoothed inference of operative latent states in Markov-switching dynamic regression data
simulateSimulate sample paths of Markov-switching dynamic regression model
forecastForecast sample paths from Markov-switching dynamic regression model