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 (Since R2021b)

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

Topics

Create Model

Creating Markov-Switching Dynamic Regression Models
Learn requirements for creating a Markov-switching dynamic regression model by using msVAR.
Create Univariate Markov-Switching Dynamic Regression Models
Create a fully or partially specified univariate Markov-switching dynamic regression model by using msVAR.
Modify msVAR Model Specifications
Adjust the specifications of a created Markov-switching dynamic regression model.
Create Multivariate Markov-Switching Dynamic Regression Models
Create a fully or partially specified Markov-switching dynamic regression model for a multivariate response process by using msVAR.

Fit Model to Data

Analyze US Unemployment Rate Using Markov-Switching Model
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

Simulate Univariate Markov-Switching Dynamic Regression Model
Generate random response and state paths from a two-state Markov-switching dynamic regression model.
Simulate Multivariate Markov-Switching Dynamic Regression Model
Generate random response and state paths from a three-state Markov-switching dynamic regression model.
Monte Carlo Simulation of Markov-Switching Dynamic Regression Model Response Variables
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.