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Tracking Chinese CPI inflation in real time

  • Michael Funke

    ()

  • Hao Yu

    ()

  • Aaron Mehrota

    ()

With recovery from the global financial crisis in 2009 and 2010, inflation emerged as a major concern for many central banks in emerging Asia. We use data observed at mixed frequencies to estimate the movement of Chinese headline inflation within the framework of a state-space model, and then take the estimated indicator to nowcast Chinese CPI infla-tion. The importance of forward-looking and high-frequency variables in tracking inflation dynamics is highlighted and the policy implications discussed.

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Paper provided by Hamburg University, Department of Economics in its series Quantitative Macroeconomics Working Papers with number 21112.

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Date of creation: Dec 2011
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Handle: RePEc:ham:qmwops:21112
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  1. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542, April.
  2. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  3. Svensson, L-E-O, 1996. "Inflation Forecast Targeting : Implementaing and Monitoring Inflation Targets," Papers 615, Stockholm - International Economic Studies.
  4. S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," NBER Working Papers 15657, National Bureau of Economic Research, Inc.
  5. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, December.
  6. Michael Funke & Aaron Mehrotra & Hao Yu, 2015. "Tracking Chinese CPI inflation in real time," Empirical Economics, Springer, vol. 48(4), pages 1619-1641, June.
  7. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  8. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  9. Guonan Ma & Yan Xiandong & Kostas Liu Xi, 2011. "China's evolving reserve requirements," BIS Working Papers 360, Bank for International Settlements.
  10. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2008. "Real-Time Measurement of Business Conditions," NBER Working Papers 14349, National Bureau of Economic Research, Inc.
  11. Andrew Filardo & Hans Genberg, 2010. "Targeting inflation in Asia and the Pacific: lessons from the recent past," BIS Papers chapters, in: Bank for International Settlements (ed.), The international financial crisis and policy challenges in Asia and the Pacific, volume 52, pages 251-273 Bank for International Settlements.
  12. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  13. Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
  14. Claudio Borio, 2011. "Central banking post-crisis: What compass for uncharted waters?," BIS Working Papers 353, Bank for International Settlements.
  15. Yunus Aksoy & Tomasz Piskorski, 2004. "U.S. Domestic Money, Inflation and Output," Macroeconomics 0401007, EconWPA.
  16. International Monetary Fund, 2009. "What Drives China’s Interbank Market?," IMF Working Papers 09/189, International Monetary Fund.
  17. George Kapetanios, 2002. "Modelling Core Inflation for the UK Using a New Dynamic Factor Estimation Method and a Large Disaggregated Price Index Dataset," Working Papers 471, Queen Mary University of London, School of Economics and Finance.
  18. Modugno, Michele, 2013. "Now-casting inflation using high frequency data," International Journal of Forecasting, Elsevier, vol. 29(4), pages 664-675.
  19. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
  20. Asimakopoulos, Stylianos & Paredes, Joan & Warmedinger, Thomas, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
  21. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, 01.
  22. Arturo Estrella & Frederic S. Mishkin, 1996. "Is There a Role for Monetary Aggregates in the Conduct of Monetary Policy?," NBER Working Papers 5845, National Bureau of Economic Research, Inc.
  23. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, Elsevier.
  24. Pierre Guérin & Massimiliano Marcellino, 2013. "Markov-Switching MIDAS Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 45-56, January.
  25. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
  26. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
  27. Libero Monteforte & Gianluca Moretti, . "Real time forecasts of inflation: the role of financial variables," Working Papers wp2011-6, Department of the Treasury, Ministry of the Economy and of Finance.
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