Advertisement

Open Economies Review

, Volume 27, Issue 5, pp 825–849 | Cite as

Chinese Divisia Monetary Index and GDP Nowcasting

  • William A. Barnett
  • Biyan Tang
Research Article

Abstract

Since China’s enactment of the Reform and Opening-Up policy in 1978, China has become one of the world’s fastest growing economies, with an annual GDP growth rate exceeding 10 % between 1978 and 2008. But in 2015, Chinese GDP grew at 7 %, the lowest rate in 5 years. Many corporations complain that the borrowing cost of capital is too high. This paper constructs Chinese Divisia monetary aggregates M1 and M2, and, for the first time, constructs the broader Chinese monetary aggregates, M3 and M4. Those broader aggregates have never before been constructed for China, either as simple-sum or Divisia. The results shed light on the current Chinese monetary situation and the increased borrowing cost of money. GDP data are published only quarterly and with a substantial lag, while many monetary and financial decisions are made at a higher frequency. GDP nowcasting can evaluate the current month’s GDP growth rate, given the available economic data up to the point at which the nowcasting is conducted. Therefore, nowcasting GDP has become an increasingly important task for central banks. This paper nowcasts Chinese monthly GDP growth rate using a dynamic factor model, incorporating as indicators the Divisia monetary aggregate indexes, Divisia M1 and M2 along with additional information from a large panel of other relevant time series data. The results show that Divisia monetary aggregates contain more indicator information than the simple sum aggregates, and thereby help the factor model produce the best available nowcasting results. In addition, our results demonstrate that China’s economy experienced a regime switch or structure break in 2012, which a Chow test confirmed the regime switch. Before and after the regime switch, the factor models performed differently. We conclude that different nowcasting models should be used during the two regimes.

Keywords

China Divisia monetary index Borrowing cost of money Nowcasting Real GDP growth rate Dynamic factor model Regime switch 

JEL Classification

C32 C38 C43 E47 E51 O53 

References

  1. Anderson RG, Jones BE (2011) A comprehensive revision of the US monetary services (Divisia) indexes. Fed Reserve Bank St. Louis Rev 83(1):51–72Google Scholar
  2. Anderson RG, Jones BE, Nesmith TD (1997a) Monetary aggregation theory and statistical index numbers. Fed Reserve Bank St. Louis Rev 79:31–51Google Scholar
  3. Anderson RG, Jones BE, Nesmith TD (1997b) Building new monetary services indexes: concepts, data, and methods. Fed Reserve Bank St. Louis Rev 79(1):53–82Google Scholar
  4. Angelini E, Camba-Mendez G, Giannone D, Reichlin L, Runstler G (2011) Short-term forecast of euro area GDP growth. Econ J 14:C25–C44Google Scholar
  5. Arnostova K, Havalant D, Luzicka L, Toth P (2011) Short-term forecasting of Czech quarterly GDP using monthly indicators. J Econ Financ 61:566–583Google Scholar
  6. Aruoba BS, Diebold FX, Scotti C (2009) Real-time measurement of business conditions. J Bus Econ Stat 27(4):417–427CrossRefGoogle Scholar
  7. Baffigi A, Golinelli R, Parigi G (2004) Bridge models to forecast the euro area GDP. Int J Forecast 20(2004):447–460CrossRefGoogle Scholar
  8. Bai J, Ng S (2002) Determining the number of factors in approximate factor models. Econometrica 70(1):191–221CrossRefGoogle Scholar
  9. Barnett WA (1978) The user cost of money. Econ Lett 2:145–149CrossRefGoogle Scholar
  10. Barnett WA (1980) Economic monetary aggregates: an application of index number and aggregation theory. J Econ 14:11–14 Reprinted in Barnett and Serletis (2000), Chapter 2, pp 11–48.CrossRefGoogle Scholar
  11. Barnett WA (1982) The optimal level of monetary aggregation. J Money Credit Banking 13(4):687–710CrossRefGoogle Scholar
  12. Barnett WA (1987) The microeconomic theory of monetary aggregation. New approaches to monetary economics. Cambridge U. Press, CambridgeCrossRefGoogle Scholar
  13. Barnett WA (2008) Monetary aggregation. In: Durlauf SN, Blume LE (ed) The new palgrave dictionary of economics. Second Edition. Palgrave MacmillanGoogle Scholar
  14. Barnett WA (2012) Getting it wrong: how faulty monetary statistics undermine the Fed, the financial system, and the economy. MIT press, CambridgeGoogle Scholar
  15. Barnett WA, Alkhareif RM (2013) Divisia Monetary Aggregates for the GCC Countries. University of Kansas Working Paper 2013Google Scholar
  16. Barnett WA, Chauvet M (2011) Financial aggregation and index number theory. World Scientific Publishing Co. Pte. Ltd, Singapore, p. 8CrossRefGoogle Scholar
  17. Barnett W, Serletis A (eds) (2000) The theory of monetary aggregation. North-Holland, AmsterdamGoogle Scholar
  18. Barnett WA, Jones BE, Nesmith TD (2008) Divisia Second Moments: An Application of Stochastic Index Number Theory, Working Paper Series in Theoretical and Applied Economics 200803. University of Kansas, Department of Economics, revised Jul 2008Google Scholar
  19. Barnett WA, Liu J, Mattson RS, van den Noort J (2013) The new CFS Divisia monetary aggregates: design, construction, and data sources. Open Econ Rev 24(1):101–124CrossRefGoogle Scholar
  20. Barnett WA, Chauvet M, Leiva-Leon D (2016) Real-time nowcasting of nominal GDP under structural break. J Econ 191(2):312–324Google Scholar
  21. Belongia MT, Ireland PN (2014) The Barnett critique after three decades: a new Keynesian analysis. J Econ 183(2014):5–21CrossRefGoogle Scholar
  22. Doz C, Giannone D, Reichlin L (2007) A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering. Discussion Paper No. 6043, Centre for Economic and Policy ResearchGoogle Scholar
  23. Engle RF, Watson MW (1981) A one-factor multivariate time series model of metropolitan wage rates. J Am Stat Assoc 76:774–781CrossRefGoogle Scholar
  24. Evans MDD (2005) Where are we now? Real-time estimates of the macroeconomy. International Journal of Central Banking 1(2):127–175.Google Scholar
  25. Fisher I (1922) The making of index numbers: the study of their varieties, tests, and reliability. Houghton Mifflin Company, BostonGoogle Scholar
  26. Forni M, Hallin M, Lippi M, Reichlin L (2000) The generalized dynamic-factor: identification and estimation. Rev Econ Stat 82(4):540–554MIT PressCrossRefGoogle Scholar
  27. Forni M, Hallin M, Lippi M, Reichlin L (2002) The generalized dynamic factor model one-sided estimation and forecasting .Working paperGoogle Scholar
  28. Geweke J (1997) The dynamic factor model analysis of economic times series. In: Aigner DJ, Goldberger AS (eds) Latent variables in socio-economic models. North-Holland, AmsterdamGoogle Scholar
  29. Geweke JF, Singleton KI (1980) Interpreting the likelihood of ratio statistic in factor models when sample size is small. J Am Stat Assoc 75:133–137CrossRefGoogle Scholar
  30. Giannone D, Reichlin L, Sala L (2004) Monetary policy in real time. NBER macroeconomic annual 2004. MIT Press, Cambridge, pp. 161–200Google Scholar
  31. Giannone D, Reichlin L, Small D (2008) Nowcasting: the real-time informational content of macroeconomic data. J Monet Econ 55(2008):665–676CrossRefGoogle Scholar
  32. Gogas P, Serletis A (2014) Divisia monetary aggregates, the great ratios, and classical money demand functions. J Money Credit Bank 46(2014):229–241Google Scholar
  33. Gogas P, Papadimitriou T, Takli E (2012) Comparison of simple sum and Divisia monetary aggregates in GDP forecasting: a support vector machines approach. The Rimini Center for Economics Analysis WP 13–04Google Scholar
  34. Hong-xia G (2007) Divisia Monetary Indexes of Aggregate Money: Measurement Method and Case Study. Author’s Master’s Thesis at Hunan University in 2007Google Scholar
  35. Istiak K, Serletis A (2015) Are the responses of the U.S. economy asymmetric to positive and negative money supply shocks? Open Econ Rev 27:303–316Google Scholar
  36. Matheson TD (2010) An analysis of the informational content of New Zealand data release: the importance of business opinion surveys. Econ Model 27(2010):304–314CrossRefGoogle Scholar
  37. Quah D, Sargent TJ (1993) A dynamic index model for large cross sections. Business Cycles, Indicators and Forecasting, pp. 285–306Google Scholar
  38. Rahman S, Serletis A (2013) The case for Divisia money targeting. Macroecon Dyn 17(2013):1638–1658Google Scholar
  39. Rahman S, Serletis A (2015) On the output effects of monetary variability. Open Econ Rev 26(2015):225–236Google Scholar
  40. Sargent TJ, Sims CA (1977) Business cycle modeling without pretending to have too much A- priori economic theory. In: Sims B (ed) New methods in business cycle research. Federal Reserve Bank of Minneapolis, MinneapolisGoogle Scholar
  41. Stcok JH, Watson MW (2002) Forecasting using principal components from a large number of predictors. J Am Stat Assoc 97(460):1167–1179CrossRefGoogle Scholar
  42. Stock JH, Watson MW (1989) New Indexes of Coincident and Leading economic Indicators. Harvard J.F. Kennedy School of Government. Paper 178dGoogle Scholar
  43. Theil H (1967) Economics and information theory. Elsevier, AmsterdamGoogle Scholar
  44. Tornqvist L (1936) The bank of Finland’s consumer price index. Bank of Finland Review 10:1–8Google Scholar
  45. Yiu MS, Chow KK (2011) Nowcasting Chinese GDP: Information Content of Economic and Financial Data. Hong Kong Institute Monetary Research working paper No.04/2011 Google Scholar
  46. Yu Q, Tsui AK (2000) Monetary services and money demand in China. China Econ Rev 11:134–148CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.University of KansasLawrenceUSA
  2. 2.Center for Financial StabilityNew YorkUSA

Personalised recommendations