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Tracking Chinese CPI inflation in real time

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  • Funke, Michael
  • Mehrotra, Aaron
  • Yu, Hao

Abstract

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 inflation. The importance of forward-looking and high-frequency variables in tracking inflation dynamics is highlighted and the policy implications discussed.

Suggested Citation

  • Funke, Michael & Mehrotra, Aaron & Yu, Hao, 2011. "Tracking Chinese CPI inflation in real time," BOFIT Discussion Papers 35/2011, Bank of Finland Institute for Emerging Economies (BOFIT).
    • Handle: RePEc:zbw:bofitp:bdp2011_035
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    1. Aksoy, Yunus & Piskorski, Tomasz, 2006. "U.S. domestic money, inflation and output," Journal of Monetary Economics, Elsevier, vol. 53(2), pages 183-197, March.
    2. Claudio Borio, 2011. "Central banking post-crisis: What compass for uncharted waters?," BIS Working Papers 353, Bank for International Settlements.
    3. Svensson, Lars E. O., 1997. "Inflation forecast targeting: Implementing and monitoring inflation targets," European Economic Review, Elsevier, vol. 41(6), pages 1111-1146, June.
    4. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    5. Michael P. Clements & David F. Hendry, 2005. "Evaluating a Model by Forecast Performance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 931-956, December.
    6. 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.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. 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.
    9. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    10. Modugno, Michele, 2013. "Now-casting inflation using high frequency data," International Journal of Forecasting, Elsevier, vol. 29(4), pages 664-675.
    11. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    12. Libero Monteforte & Gianluca Moretti, 2013. "Real‐Time Forecasts of Inflation: The Role of Financial Variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 51-61, January.
    13. 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.
    14. Pierre Guérin & Massimiliano Marcellino, 2013. "Markov-Switching MIDAS Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 45-56, January.
    15. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 17, pages 961-982, Elsevier.
    16. 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.
    17. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    18. 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.
    19. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    20. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    21. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    22. Estrella, Arturo & Mishkin, Frederic S., 1997. "Is there a role for monetary aggregates in the conduct of monetary policy?," Journal of Monetary Economics, Elsevier, vol. 40(2), pages 279-304, October.
    23. 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.
    24. 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.
    25. 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, January.
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    2. Chronis, George A., 2016. "Modelling the extreme variability of the US Consumer Price Index inflation with a stable non-symmetric distribution," Economic Modelling, Elsevier, vol. 59(C), pages 271-277.
    3. Marlene Amstad & Ye Huan & Guonan Ma, 2014. "Developing an underlying inflation gauge for China," Working Papers 853, Bruegel.
    4. Minghong Tan, 2014. "The Transition of Farmland Production Functions in Metropolitan Areas in China," Sustainability, MDPI, vol. 6(7), pages 1-14, June.
    5. Ting-Ting Sun & Chi-Wei Su & Ran Tao & Meng Qin, 2021. "Are Agricultural Commodity Prices on a Conventional Wisdom with Inflation?," SAGE Open, , vol. 11(3), pages 21582440211, August.

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    More about this item

    Keywords

    Nowcasting; CPI inflation cycle; mixed-frequency modelling; dynamic factor model; China;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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