Dynamic Analysis of Time Series Data Based on State Space Model

Abstract

The state space model is effective on analyzing non-stationary time series data, especially in adapting better to the dynamic variation analysis of the time series data and forecasting demand,by replacing fixed parameters with the variable ones.This article elaborates the constructing process of state space model by the measurement equation and state equation. This article also selects M0 money supply, M1 money supply, M2 money supply as the characterize variables of monetary policy, selects the national housing climate Index as characterize variables of real estate development,status regression model with stronger dynamic analysis capabilities as empirical analysis tool, with 2005 to 2012 monthly data of relevant variables as empirical analysis object, carry out the empirical study of relationship between the development of China's real estate industry and the amount of the three currencies. The empirical results show that the amount of three currencies elastic influence for real estate development are positive, M2 money supply impact of greater intensity. Among, M0 money supply influence gradually weakened, M1 and M2 money supply influence gradually increased.