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Markov-Switching Dynamic Regression Models

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

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msVARCreate Markov-switching dynamic regression model (Since R2019b)
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 (Since R2019b)
summarizeSummarize Markov-switching dynamic regression model estimation results (Since R2021b)
filterFiltered inference of operative latent states in Markov-switching dynamic regression data (Since R2019b)
smoothSmoothed inference of operative latent states in Markov-switching dynamic regression data (Since R2019b)
simulateSimulate sample paths of Markov-switching dynamic regression model (Since R2019b)
forecastForecast sample paths from Markov-switching dynamic regression model (Since R2019b)

Topics

Create Model

Fit Model to Data

Generate Monte Carlo Simulations