The Characteristics And Identifying The Turning Points Of Business Cycles In China

Since the founding of the PRC, the China macro-economy has developed in the procession of continuous fluctuation, in which there are many serious overheated economy and economic recessions. So it is difficult to make China economy to grow sustainably and stably in the long term. Two premises the government can succeed in achieving the macro-control are to grasp the characters of the business cycles fluctuation and to identify timely the turning points of the business cycles; so the measurement of the characters of the business cycles and the identifying of the turning points of the business cycles are especially important.In general, the details and the depth of business cycles fluctuation analysis and measurement are not enough, and some important issues are neglected. Therefore, this paper studies systematically and comprehensively the China business cycles fluctuation, based on the existing research results at home and aboard, and applied the international more advanced theories and methods of the business cycles fluctuation measurement and the identifying the turning points. Namely, this paper studies on the character of China business cycles fluctuation and the identifying the turning points of business cycles. Compared with the existing research results, innovations and significance in this paper lie in:(1) The innovation of study perspective. It is first time to study the identifying the turning points of China business cycles based on the enterprises micro financial data of China industrial enterprises database. So we filter the five macro time series by three filtering methods, and compare the original sequence, the trend components and the cycle components and the relations among them, compare the turning points of between the cycle components and the growth rate of business cycles. (2) The innovation of the research methods. Compared with the single-equation smooth transition panel model, multiple levels smooth transition panel model shows more advantages in study of the character of business cycles. Applied multiple levels smooth transition panel model, based on the enterprises micro financial data of China industrial enterprises database, this paper has detected the inflexion of China industrial business cycles, and concluded that there are non-synchronous entering (exiting) recessions in different industrial. This has great significance for judging the economic situation. Besides using the lagged terms of explanatory variables as transition variables, the paper selects the lagged terms of the fixed investments as transition variables in STAR model of GDP, and succeeds in identifying the turning points of business cycle, in order to analyze the effects of the fixed investments on GDP. (3) The study conclusions of this paper have more rich economic and policy implications. The results of exponential smooth conversion show that the structure change of China GDP occurs in one lag of its own; The conversion parameter 2.612 shows that the growth of China GDP has the character of slower adjustment and conversion; The positional parameter 0.096 shows that 9.6% growth of GDP is on the intermediate state or the critical level of expansion and conversion, the values of G in six zones are more than 0.8, besides a summit of growth rate in year 1993-1995, and the turning points of the remarkable transition in China business cycles in other five zones are at the bottoms of U-form. The results of the auto-regression of the logic smooth transition show that the structure change of the China fixed investment and urban residents'consumption occurs in four lags of its own, then it manifests that the growth of the two macro variables mostly depends on their own historical levels of growth; The conversion parameter 4.16 measures the rate of structure transition, and shows that the growth of the fixed investments has the character of quicker adjustment and conversion; The positional parameter 0.19 shows that 19% growth of the fixed investments is on the intermediate state or the critical level of expansion and conversion. Using the fixed investments as the transition parameter of the logic smooth transition model of GDP, The conversion parameter 1.15 measures the rate of structure transition, then it shows that the fixed investments of China has pulling effects on GDP, and has the character of slower adjustment and conversion; The turning point identified by it goes one year earlier than those identified by other methods, so the turning points of GDP can be identified in advance. And it also shows that the economy of China has been in an investment-led stage since Reformation and Opening. The economic growth relies on the investments the most among three factors fueling the economic growth:investments, consumptions and exports, so the fluctuation of investments significantly affects the output. However, the five lags of the fixed investments used as the transition parameter in the model show that the fixed investments don't affect immediately the output, because of the hysteretic effect, affect gradually, the regime switching of GDP will occur in five years or so. The regression results of multiple levels smooth transition panel model manifest that there are non-synchronous entering (exiting) recessions in different industrial, and the recovery sequence of the different industrial is listed, then we can discover that manufacturing industry 1, manufacturing industry 2, manufacturing industry 9, electric power, fuel gas and water production and supply industry take the lead to go out of the recession, it shows that the four industry are sensitive to the economic change, so they can be the leading indicators in the judgment of macro-economy. The identifying the turning points of business cycle are based on the leading industries trend. Obviously, the traditional linear methods cannot depict these characters; all of these discoveries are the conclusion based on the nonlinear STR model. In this sense, this paper has remarkable academic and applied significance.