Research On Macroeconomic Early Warning System

Macroeconomic Operation is a very complex system, the economic fluctuation will bring adverse effects to sustained and healthy economic development. Therefore it is of great importance to build macroeconomic early warning system and to take effective measures to avoid the fluctuations.Economic early warning is based on the economic cycle theory, it is the theory and method on economic monitor and economic forecasting,which is centering on economic periodic fluctuation.Under the guidance of Economic Theories of early warning, this paper introduces the development history of economic early warning theory,summarizes the economic early warning methods.On the basis of Jiangsu’s macroeconomic data for the empirical analysis,this paper accomplishes the research and construction work on macroeconomic early warning system of Jiangsu Province.Firstly, based on foreign macroeconomic early warning index system,combined the design principles and macroeconomic characteristics of Jiangsu Province,the present work select index number which contribute to the economic activity most.And then applying TRAMO/SEATS Method to do the seasonal adjustment for the index,to eliminate seasonal factors and irregular factors;using the H-P filtering method to do the further separation to get cycle fluctuations; using the Time Difference Correlation Analytic Method to classify the various indicators and finally building prosperity index system for Jiangsu real economic environment.Secondly, according to the two methods of early warning system building, this paper using composite index method to integrate three kinds of indexes, eventually forming the first composited index, consistent composited index, lagged composited index,to reflect the current macro-economic boom.Then chooses appropriate indicators, do the boom alarm limit analysis to each indicator,finally build the comprehensive early warning index to show the overall economy of Jiangsu Provincejudge the future economy development in Jiangsu Province.