Parameter estimation of MS-DFM model and its application in stock market cycle recognition
A novel parameter estimation method,the two-step expectation-maximization(EM)approach,is presented for existing Markov switching dynamic factor models.The Markov switching dynamic factor models are re-parameterized to transform them into a mixed dynamic factor model,with both factors and states treated as latent variables,the EM algorithm is employed to estimate the parameters and factor scores in the re-parameterized model.With factor scores taken as known data and states as latent variables,Markov switching auto-regressive models are established for each factor sequence.The EM algorithm is utilized to estimate the intercept terms and auto-regressive coefficients that vary with states,and to identify states and turning points.The effectiveness of this method is verified through numerical simulations.The model and estimation methods are applied to analyze a stock market data set from the Shanghai and Shenzheng stock exchanges in China,aiming to measure and identify industry cycles in the stock market.
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