IDEAS home Printed from https://ideas.repec.org/p/fip/fedawp/2016-07.html
   My bibliography  Save this paper

Forecasting China's Economic Growth and Inflation

Author

Listed:

Abstract

Although macroeconomic forecasting forms an integral part of the policymaking process, there has been a serious lack of rigorous and systematic research in the evaluation of out-of-sample model-based forecasts of China's real gross domestic product (GDP) growth and consumer price index inflation. This paper fills this research gap by providing a replicable forecasting model that beats a host of other competing models when measured by root mean square errors, especially over long-run forecast horizons. The model is shown to be capable of predicting turning points and usable for policy analysis under different scenarios. It predicts that China's future GDP growth will be of an L-shape rather than a U-shape.

Suggested Citation

  • Higgins, Patrick C. & Zha, Tao & Zhong, Karen, 2016. "Forecasting China's Economic Growth and Inflation," FRB Atlanta Working Paper 2016-7, Federal Reserve Bank of Atlanta.
  • Handle: RePEc:fip:fedawp:2016-07
    as

    Download full text from publisher

    File URL: https://www.frbatlanta.org/-/media/Documents/research/publications/wp/2016/07.pdf
    File Function: Full text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    2. Jonathan H. Wright, 2013. "Unseasonal Seasonals?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(2 (Fall)), pages 65-126.
    3. Ben S. Bernanke & Mark Gertler & Mark Watson, 1997. "Systematic Monetary Policy and the Effects of Oil Price Shocks," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 28(1), pages 91-157.
    4. Haiyan Ding & Hui He, 2018. "A Tale of Transition: An Empirical Analysis of Economic Inequality in Urban China, 1986-2009," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 29, pages 106-137, July.
    5. Fernald, John G. & Spiegel, Mark M. & Swanson, Eric T., 2014. "Monetary policy effectiveness in China: Evidence from a FAVAR model," Journal of International Money and Finance, Elsevier, vol. 49(PA), pages 83-103.
    6. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148 Elsevier.
    7. Chen, Kaiji & Waggoner, Daniel F. & Higgins, Patrick C. & Zha, Tao, 2016. "Impacts of Monetary Stimulus on Credit Allocation and Macroeconomy: Evidence from China," FRB Atlanta Working Paper 2016-9, Federal Reserve Bank of Atlanta, revised 01 Oct 2017.
    8. Ivan Roberts & Graham White, 2015. "Seasonal Adjustment of Chinese Economic Statistics," RBA Research Discussion Papers rdp2015-13, Reserve Bank of Australia.
    9. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    11. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    12. Robertson, John C & Tallman, Ellis W, 2001. "Improving Federal-Funds Rate Forecasts in VAR Models Used for Policy Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 324-330, July.
    13. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(2), pages 1-78.
    14. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    15. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    16. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    17. Sims, Christopher A. & Zha, Tao, 2006. "Does Monetary Policy Generate Recessions?," Macroeconomic Dynamics, Cambridge University Press, vol. 10(02), pages 231-272, April.
    18. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    19. Chun Chang & Kaiji Chen & Daniel F. Waggoner & Tao Zha, 2016. "Trends and Cycles in China's Macroeconomy," NBER Macroeconomics Annual, University of Chicago Press, vol. 30(1), pages 1-84.
    20. Jonathan H. Wright, 2013. "Unseasonal Seasonals?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(2 (Fall)), pages 65-126.
    21. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    22. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    23. 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.
    24. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
    25. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    26. Tao Zha, 1998. "A dynamic multivariate model for use in formulating policy," Economic Review, Federal Reserve Bank of Atlanta, issue Q 1, pages 16-29.
    27. Andrew Bauer & Robert A. Eisenbeis & Daniel F. Waggoner & Tao Zha, 2006. "Transparency, expectations and forecasts," Economic Review, Federal Reserve Bank of Atlanta, issue Q 1, pages 1-25.
    28. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:eee:chieco:v:46:y:2017:i:c:p:110-122 is not listed on IDEAS
    2. Santaeulàlia-Llopis, Raül ; Zheng, Yu, 2016. "The Price of Growth: Consumption Insurance in China 1989-2009," Economics Working Papers ECO2016/13, European University Institute.
    3. Chen, Kaiji & Waggoner, Daniel F. & Higgins, Patrick C. & Zha, Tao, 2016. "Impacts of Monetary Stimulus on Credit Allocation and Macroeconomy: Evidence from China," FRB Atlanta Working Paper 2016-9, Federal Reserve Bank of Atlanta, revised 01 Oct 2017.
    4. Tao Zha & Kaiji Chen, 2017. "The Asymmetric Transmission of China's Monetary Policy," 2017 Meeting Papers 516, Society for Economic Dynamics.

    More about this item

    Keywords

    out of sample; policy projections; scenario analysis; probability bands; density forecasts; random walk; Bayesian priors;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedawp:2016-07. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Elaine Clokey). General contact details of provider: http://edirc.repec.org/data/frbatus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.