Markov Switching Models | Switching Models in Econometrics, Part 1

This is the first video in a two-part series that shows how to model time series data in the presence of regime shifts in MATLAB. In this video, William Mueller uses Markov switching models from the Econometrics Toolbox to model unemployment data across different economic regimes.

Download the code: https://bit.ly/3vsM73r

Video Series:
Switching Models in Econometrics:    • Switching Models in Econometrics  

Highlights:
Import and visualize unemployment data across regimes.
Set up ARIMA or vector auto-regressive sub models to describe the data in each economic state.
Create a discrete-time Markov switching mechanism using the Econometrics Toolbox to transition between different economic states.
Visualize the transition probabilities as well as the switching mechanism for the regime shifts using Markov chain graph plotting functionality.
Assemble a switching model using built-in functionality in the Econometrics Toolbox to switch between the sub models in different macroeconomic regimes.
Estimate model parameters, simulate the estimated model over multiple paths and time periods, and forecast the estimated model from the unemployment data.

What Is Econometrics Toolbox?: https://bit.ly/3kosuD5

Econometrics Toolbox includes tools to model time series data in the presence of regime shifts in MATLAB. This video series, along with the associated code, illustrate how you can use Markov switching models or threshold switching models to model unemployment and inflation rate data across different economic regimes.

Related Products
MATLAB: https://bit.ly/39sQXVN
Econometrics Toolbox: https://bit.ly/3LCmEu8
Statistics and Machine Learning Toolbox: https://bit.ly/3OX7u4G

0:00 Introduction
0:19 What is a Switching Model?
3:13 Data Regimes: Unemployment Rate
6:28 Submodel Arrays
7:54 ARIMA Submodels
9:35 VARM Submodels
10:35 Matlab Classes and Methods
11:46 Stochastic Switching: Markov Chains
15:05 Constructing a Markov Switching Model
19:41 Model Estimation
25:22 Model Simulation
27:14 Model Forecasting
28:45 Documentation and Further Examples
29:32 Conclusion


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