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Forecasting China's Economic Growth and Inflation

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  • Patrick Higgins
  • Tao Zha
  • Karen Zhong

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 GDP growth and CPI 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 to be usable for policy analysis under different scenarios. It predicts that China's future GDP growth will be of L-shape rather than U-shape.

Suggested Citation

  • Patrick Higgins & Tao Zha & Karen Zhong, 2016. "Forecasting China's Economic Growth and Inflation," NBER Working Papers 22402, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:22402
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    References listed on IDEAS

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    1. 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.
    2. Kaiji Chen & Patrick C. Higgins & Daniel F. Waggoner & Tao Zha, 2016. "Impacts of Monetary Stimulus on Credit Allocation and Macroeconomy: Evidence from China," FRB Atlanta Working Paper 2016-9, Federal Reserve Bank of Atlanta.
    3. Ivan Roberts & Graham White, 2015. "Seasonal Adjustment of Chinese Economic Statistics," RBA Research Discussion Papers rdp2015-13, Reserve Bank of Australia.
    4. 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.
    5. 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.
    6. Sims, Christopher A. & Zha, Tao, 2006. "Does Monetary Policy Generate Recessions?," Macroeconomic Dynamics, Cambridge University Press, vol. 10(2), pages 231-272, April.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Andrew Bauer & Robert A. Eisenbeis & Daniel F. Waggoner & Tao Zha, 2006. "Transparency, expectations and forecasts," Economic Review, Federal Reserve Bank of Atlanta, vol. 91(Q 1), pages 1-25.
    12. Jonathan H. Wright, 2013. "Unseasonal Seasonals?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(2 (Fall)), pages 65-126.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Jonathan H. Wright, 2013. "Unseasonal Seasonals?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(2 (Fall)), pages 65-126.
    18. 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.
    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. 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.
    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. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, vol. 84(Q1), pages 4-18.
    23. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    24. Tao Zha, 1998. "A dynamic multivariate model for use in formulating policy," Economic Review, Federal Reserve Bank of Atlanta, vol. 83(Q 1), pages 16-29.
    25. Fernandez, Roque B, 1981. "A Methodological Note on the Estimation of Time Series," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 471-476, August.
    26. 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.
    27. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    28. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    29. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
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    Cited by:

    1. Shi, Jinchuan & Zhang, Xiaoqian, 2018. "How to explain corporate investment heterogeneity in China's new normal: Structural models with state-owned property rights," China Economic Review, Elsevier, vol. 50(C), pages 1-16.
    2. Raül Santaeulàlia-Llopis & Yu Zheng, 2018. "The Price of Growth: Consumption Insurance in China 1989–2009," American Economic Journal: Macroeconomics, American Economic Association, vol. 10(4), pages 1-35, October.
    3. Yu, Mingzhe & Fan, Jiachuan & Wang, Haijun & Wang, Jie, 2023. "US trade policy uncertainty on Chinese agricultural imports and exports: An aggregate and product-level analysis," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 70-83.
    4. Yang, Xingquan & Han, Liang & Li, Wanli & Yin, Xingqiang & Tian, Lin, 2017. "Monetary policy, cash holding and corporate investment: Evidence from China," China Economic Review, Elsevier, vol. 46(C), pages 110-122.
    5. Kaiji Chen & Patrick C. Higgins & Daniel F. Waggoner & Tao Zha, 2016. "Impacts of Monetary Stimulus on Credit Allocation and Macroeconomy: Evidence from China," FRB Atlanta Working Paper 2016-9, Federal Reserve Bank of Atlanta.
    6. Tao Zha & Kaiji Chen, 2017. "The Asymmetric Transmission of China's Monetary Policy," 2017 Meeting Papers 516, Society for Economic Dynamics.
    7. Yucheng Yang & Yue Pang & Guanhua Huang & Weinan E, 2020. "The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data," Papers 2010.05172, arXiv.org.
    8. Xiuyun Yang & Muhammad Nouman Shafiq, 2020. "The Impact of Foreign Direct Investment, Capital Formation, Inflation, Money Supply and Trade Openness on Economic Growth of Asian Countries," iRASD Journal of Economics, International Research Alliance for Sustainable Development (iRASD), vol. 2(1), pages 25-34, June.
    9. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    10. Kai Xu & Bart Bossink & Qiang Chen, 2019. "Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    11. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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