Hidden Markov Model for Predicting the Turning Points of GDP Fluctuation

Abstract

-At present, the methods of predicting the turning points of GDP fluctuation is difficult to choose suitable influence indexes, and emphasize static function dependency or dynamic propagation of time series so that the static and dynamic information can not be consistently combined. In this paper, hidden Markov model is introduced for predicting the turning points of GDP fluctuation. The real GDP data of China from 1990 to 2013 is used for modeling and the experimental results show that hidden Markov model has good practicability and reliability.

The Power of a Leading Indicators Fluctuation Trend for Forecasting Taiwans Real Estate Business Cycle: An Application of a Hidden Markov Model

“…According to Hassan and Nath (2005) the HMM model has the following advantages: (1) a strong statistical basis; (2) the ability to handle new information robustly; (3) the capacity to calculate and forecast similarity modes more efficiently. In recent years, the HMM model has been applied in economic, financial, and management fields, including economics in general (Leng and Wang, 2014) and the study of financial series fluctuations more specifically (Gregoir and Lenglart, 2000;Hassan and Nath, 2005;Korolkiewicz and Elliott, 2008;Oguz and Gurgen, 2008;Liu, 2010;Roamn et al, 2010;Zhu and Cheng, 2013). In the management field, it has been used to study customer relations management (Bouchaffra and Tan, 2004;Shen and Zhao, 2006;Netzer et al, 2008;Sepideh and Aaghaie, 2011) and online purchasing behaviors (Wu et al, 2005).…”