MARKOV SWITCHING DYNAMIC REGRESSION MODEL: APPLICATION TOFINANCIAL DATA

Abstract

Hidden Markov Models are used to study occurrences where a portion of the phenomenon is observable while the rest is unobservable. The model consists of two sets of random variables, namely, unobservable and observable random variables. Markov models are usually used to model the unobservable variable part of the hidden Markov model; a time-series regression model is used to model the observable part of the model. Then, the two random variables produce a regime-switching model known as the Markov-switching dynamic regression model with a distinct probability distribution. The parameters of this resulting model can be estimated using the maximum likelihood estimation method, and the characteristics of the regression model vary with the presiding regime of the model. The regression objective of this work is to explain the variability in the Nigerian monthly inflation rates using the variability in the average monthly United States dollar exchange rates. The correlation between the two datasets is determined using the least squares regression model and the Markov switching dynamic regression model. The Markov switching regression (Regime 2) model represents the highvariance regime. It explains the regression objectives more than the OLS regression model, as shown in the results. Consequently, it shows that when the Markov state model is in the high variance regime, Nigeria's economy shows a recession. The results depict that the regime-switching model outperformed the single regime model in capturing the properties of the data from the comparative analysis.