A Dynamic Programming Algorithm for MSM Based on Markov Loss Function
In this paper, we propose a dynamic programming algorithm based on Markov loss for the state sequence prediction problem of Markov regime switching model. This algorithm aims to improve the accuracy of state sequence prediction by comprehensively considering global and marginal loss. Through numerical simulation experiments and empirical research, the predictive performance of the Viterbi algorithm, marginal loss and Markov loss are evaluated within the MS-AR(1). The derived results exhibit that the algorithm holds considerable superiority in forecasting real state sequences and effectively mitigates state error transition rates.
Jan 1, 0001