This repository provides functions (and examples scripts) for the estimation, simulation and forecasting of a general Markov Regime Switching Regression in Matlab.
Before using the package, make sure you read the pdf file (About the MS_Regress_Package.pdf) in the downloaded zip file. A copy of this paper can be found in SSRN.
First, clone this repository or download it as a zip file (see download choice in right side button of the webpage).
Matlab works by reading files in the search path. In order to use the functions of MS_Regress, all you need to do is to tell matlab to look for the files in the m_Files folder (e.g. addpath('m_Files') of the zip file. After that, all functions will be available to the user.
The easiest way to get started is to run the example scripts provided in the root folder. They should work as is, without any modification. You can modify the examples for you own dataset and custom model.
I also wrote a R version of the package (fMarkovSwitching). It is public available in the R Metrics project and in R Code section of my website. Please be aware that the R version in no longer being maintained so it is actually an older version of the matlab package with only the basic features.
- Support for univariate and multivariate models;
- Support of any number of states and any number of explanatory variables;
- Estimation, by maximum likelihood, of any type of switching setup for the model. This means that you can choose which coefficients in the model, including distribution parameters, are switching states over time;
- A wrapper function for the estimation of regime switching autoregressive models, including multivariate case (MS-VAR) is included in the package;
- The values of parameter's standard errors can be calculated with 2 different methods;
- Includes a C version of hamilton’s filter that may be used for speeding up the estimation function (see pdf for details);
- Possibility of three distinct distribution assumptions for residual vector (Normal, t or GED);
- The user can choose the optimizing function to be used in the estimation of the model (fminsearch, fmincon or fminunc);
- Support for reduced/constrained estimation (see pdf document for details);
- Several example scripts that show how to use the code;
- The EM algorithm is not implemented (all models are estimated by direct maximization of log likelihood function);
- It does not support state space models with markov switching effects;
- It cannot estimate a model with time varying transition probabilities (TVPT). But, Zhuanxin Ding has developed a matlab package for TVTP models based on MS_Regress. You can access it here;
- It does not support models with garch type of filters for conditional volatility;
Optimization, Statistics
Revision 17/04/2017 (ver 1.11)
- Fixed nargin bug
- Fixed constCoeff bug
Revision 19/04/2015 (ver 1.09)
- Fixed typos in manual
Revision 30/10/2014 (ver 1.08)
- Fixed bug in calculation of transition matrix standard error when using fminsearch and fminunc (thanks Wembo Zhou for pointing it out)
Revision 21/02/2014 (ver 1.07)
- Fixed small bug in calculation of standard deviation forecast
Revision 15/07/2013 (ver 1.06)
- Fixed small bug in the error checking part of the code
Revision 20/05/2013 (ver 1.05)
- Fixed bug in constrained estimation (the use of the new optimizer were in conflict with old algorithm)
Revision 25/01/2013 (ver 1.04)
- Fixed bug in the "print screen" function for MS-VAr models
Revision 13/01/2013
- Fixed bug in the calculation of GED probability distribution
Revision 19/09/2012
- Fixed bug in the estimation of models with t and GED distribution with the new algorithm
Revision 19/09/2012
- Fixed bug on standard error calculation (this only affects the code after last update (09/08/2012), the previous version is Ok).
Revision 09/08/2012
- Added the possibility of user choosing the optimization function (fmincon, fminsearch or fminunc)
- Changes in pdf manual
Revision 30/05/2012
- Improvements in pdf manual
Alexander, C. (2008) ‘Market Risk Analysis: Practical Financial Econometrics’ Wiley.
Brooks, C. (2002) ‘Introduction to Econometrics’ Cambridge University Press.
Hamilton, J., D. (2005) Regime Switching Models. Palgrave Dictionary of Economics, (available at http://dss.ucsd.edu/~jhamilto/palgrav1.pdf )
Hamilton , J., D. (1994) ‘Time Series Analysis’ Princeton University Press.
Kim, C., J., Nelson, C., R. (1999) State Space Model with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. The MIT press.