The Macroeconomic Leading Indicators Analysis Based on the Method of K–L Information Content and Time Difference Correlation Coefficient

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 241)

Abstract

It is common to have fluctuations of development and growth level in the process of economic development. Thus it is quite important to found a reasonable macro economic leading indicators to analyze the macro economy. First, we analyzed the composition of the proportion of GDP and chose the composite index of over scaled industry increasing value and total retail sales of social consumer goods which make up great amount of GDP as reference index. Then, we use K–L information content and time difference correlation coefficient to analyze the macro economic indicators, Through the method we chose the indicators which have the leading feature. Finally, by the help of composite index in the method of economic sentiment index, we had the leading indicators. The empirical analysis shows that the leading indicators for Chengdu can predict the trend of the macro economy in Chengdu in a way, so it can help the government to make decisions in advance.

Keywords

Leading indicator K–L information content Time difference correlation coefficient Composite index Macro economy 

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References

  1. 1.
    Xie J, Wang B (2007) Chian’s macro economic early warning system of prosperity monitoring. Statistics and Decision 2:122–124 (In Chinese)Google Scholar
  2. 2.
    Wang L (1998) Economic cycle research. Economic Science Press, Beijing (In Chinese)Google Scholar
  3. 3.
    He W, Liu C (2007) China’s macro economic leading indicator system and its empirical study for economic predicting. South China Finance 4:14–18 (In Chinese)Google Scholar
  4. 4.
    Lahiri K, Geoffrey HM (1991) Leading economic indicators: New approaches and forecasting Records. Cambridge University PressGoogle Scholar
  5. 5.
    Introducing the CEMAC-Goldman Scales Coincident and Leading Indicators for China (2004) Asia-Pacific Economics Analyst 11:3–26Google Scholar
  6. 6.
    Ding W (2004) Research on Beijing’s leading indicators. Journal of Shanxi Finance and Economics University 4:38–45 (In Chinese)Google Scholar
  7. 7.
    Zhang S, Ge X (1991) Primary research on new method for pre-warning macro economy. Quantitative & technical economics 8:23–26 (In Chinese)Google Scholar
  8. 8.
    Li B (2002) National economic statistics. China Statistics Press, Beijing (In Chinese)Google Scholar
  9. 9.
    Otrok, C, Whiteman CH (1998) Bayesian leading indicators: Measuring and predicting economic conditions in Iowa. International Economic Review 997–1014Google Scholar
  10. 10.
    Stock JH, Watson MW (1993) A procedure for predicting recessions with leading indicators: Econometric issues and recent experience. National Bureau of Economic Research January 95–156Google Scholar
  11. 11.
    Banerjee A, Marcellino M (2006) Are there any reliable leading indicators for US inflation and GDP growth? International Journal of Forecasting January-March 22(1):137–151Google Scholar
  12. 12.
    Yan L,WuW(2005) Reference and thinking: The comparison between domestic and foreign economic leading indicator system. Journal of Financial Research 9:45–50 (In Chinese)Google Scholar
  13. 13.
    Zhu J, Wang C (1993) Theoretical method for pre-warning system of economic analysis. China Planning Press, Beijing (In Chinese)Google Scholar
  14. 14.
    Dong W, Gao T (1998) Economic cycle fluctuation analysis and forecasting method. Jilin University Press, Changchun (In Chinese)Google Scholar
  15. 15.
    OECD Department of Economics and Statistics (1987) OECD leading indicators and business cycles in member countries 1960–1985, Sources and Methods 39:38–42Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Business SchoolSichuan UniversityChengduPeople’s Republic of China

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