Statistical Estimation and Classification Algorithms for Regime-Switching VAR Model with Exogenous Variables
AbstractWe consider a vector autoregression model with exogenous variables and Markov-switching regimes to describe complex systems with cyclic changes of states. To estimate and forecast the states, we propose EM and discriminant analysis algorithms based on non-classified and classified data samples accordingly. The accuracy of the algorithms is examined by means of computer simulation experiments.
Bellman R, Dreyfus S (1962). Applied Dynamic Programming. Princeton University Press Princeton, New Jersey.
Bilmes J (1998). A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Int. Computer Science Institute, Berkeley CA.
Hamilton J (2008). Regime-switching Models. New Palgrave Dictionary of Economics, pp. 1755-1804.
Kharin Y (1996). Robustness in Statistical Pattern Recognition. Dordrecht, Boston, London Kluwer Academic Publishers.
Krolzig H (1997). Markov Switching Vector Autoregressions. Modelling Statistical Inference and Application to Business Cycle Analysis. Berlin, Springer-Verlag.
Lutkepohl H (2005). New Introduction to Multiple Time Series Analysis. Berlin, Springer-Verlag.
Malugin V (2014). Methods of Analysis of Multivariate Econometric Models with Heterogeneous Structure. Minsk, Belarusian State University.
Malugin V, Kharin Y (1986). On Optimal Classification or Random Observations Different in Regression Equations. Automation and Remote Control, (7), 61-69.
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