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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Create Model

`msVAR`	Create Markov-switching dynamic regression model
`dtmc`	Create discrete-time Markov chain
`arima`	Create univariate autoregressive integrated moving average (ARIMA) model
`varm`	Create vector autoregression (VAR) model

Fit Model to Data

`estimate`	Fit Markov-switching dynamic regression model to data
`summarize`	Summarize Markov-switching dynamic regression model estimation results

Infer Latent States

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

Generate Monte Carlo Simulations

simulate Simulate sample paths of Markov-switching dynamic regression model

Generate Forecasts

forecast Forecast sample paths from Markov-switching dynamic regression model