IDEAS home Printed from https://ideas.repec.org/p/syb/wpbsba/2123-20406.html
   My bibliography  Save this paper

Predicting China’s Monetary Policy with Forecast Combinations

Author

Listed:
  • Pauwels, Laurent

Abstract

China’s monetary policy is unconventional and constantly evolving as a result of its rapid economic development. This paper proposes to use forecast combinations to predict the People’s Bank of China’s monetary policy stance with a large set of 73 macroeconomic and financial predictors covering various aspects of China’s economy. The multiple instruments utilised by the People’s Bank of China are aggregated into a Monetary Policy Index (MPI). The intention is to capture the overall monetary policy stance of the People’s Bank of China into a single variable that can be forecasted. Forecast combination assign weights to predictors according to their forecasting performance to produce a consensus forecast. The out-of-sample forecast results demonstrate that optimal forecast combinations are superior in predicting the MPI over other models such as the Taylor rule and simple autoregressive models. The corporate goods price index and the US nominal effective exchange rate are the most important predictors.

Suggested Citation

  • Pauwels, Laurent, 2019. "Predicting China’s Monetary Policy with Forecast Combinations," Working Papers BAWP-2019-07, University of Sydney Business School, Discipline of Business Analytics.
  • Handle: RePEc:syb:wpbsba:2123/20406
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/2123/20406
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sun, Rongrong, 2013. "Does monetary policy matter in China? A narrative approach," China Economic Review, Elsevier, vol. 26(C), pages 56-74.
    2. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    3. Güneş Kamber & Madhusudan Mohanty, 2018. "Do interest rates play a major role in monetary policy transmission in China?," BIS Working Papers 714, Bank for International Settlements.
    4. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    5. Dong He & Laurent L. Pauwels, 2008. "What Prompts the People's Bank of China to Change Its Monetary Policy Stance? Evidence from a Discrete Choice Model," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 16(6), pages 1-21, November.
    6. Glick, Reuven & Hutchison, Michael, 2009. "Navigating the trilemma: Capital flows and monetary policy in China," Journal of Asian Economics, Elsevier, vol. 20(3), pages 205-224, May.
    7. Chen, Hongyi & Chow, Kenneth & Tillmann, Peter, 2017. "The effectiveness of monetary policy in China: Evidence from a Qual VAR," China Economic Review, Elsevier, vol. 43(C), pages 216-231.
    8. Xiong, Weibo, 2012. "Measuring the monetary policy stance of the People's bank of china: An ordered probit analysis," China Economic Review, Elsevier, vol. 23(3), pages 512-533.
    9. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
    10. Hu, Ling & Phillips, Peter C. B., 2004. "Nonstationary discrete choice," Journal of Econometrics, Elsevier, vol. 120(1), pages 103-138, May.
    11. repec:hal:journl:peer-00834423 is not listed on IDEAS
    12. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    13. 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.
    14. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    15. Hyeongwoo Kim & Wen Shi & Kwang-Myoung Hwang, 2016. "Estimating interest rate setting behaviour in Korea: a constrained ordered choices model approach," Applied Economics, Taylor & Francis Journals, vol. 48(23), pages 2199-2214, May.
    16. Eric Girardin & Sandrine Lunven & Guonan Ma, 2017. "China's evolving monetary policy rule: from inflation-accommodating to anti-inflation policy," BIS Working Papers 641, Bank for International Settlements.
    17. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    18. Hyeongwoo Kim & John Jackson & Richard Saba, 2009. "Forecasting the FOMC's interest rate setting behavior: a further analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 145-165.
    19. Dong He & Honglin Wang & Xiangrong Yu, 2015. "Interest Rate Determination in China: Past, Present, and Future," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 255-277, December.
    20. Andrey Vasnev & Margaret Skirtun & Laurent Pauwels, 2013. "Forecasting Monetary Policy Decisions in Australia: A Forecast Combinations Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(2), pages 151-166, March.
    21. Heikki Kauppi, 2012. "Predicting the Direction of the Fed's Target Rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(1), pages 47-67, January.
    22. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    23. Stefan Gerlach, 2007. "Interest Rate Setting by the ECB, 1999-2006: Words and Deeds," International Journal of Central Banking, International Journal of Central Banking, vol. 3(3), pages 1-46, September.
    24. Nathan Porter & TengTeng Xu, 2016. "Money-Market Rates and Retail Interest Regulation in China: The Disconnect between Interbank and Retail Credit Conditions," International Journal of Central Banking, International Journal of Central Banking, vol. 12(1), pages 143-198, March.
    25. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    26. Kim, Hyeongwoo & Shi, Wen, 2018. "The determinants of the benchmark interest rates in China," Journal of Policy Modeling, Elsevier, vol. 40(2), pages 395-417.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hyeongwoo Kim & Wen Shi, 2014. "The Determinants of the Benchmark Interest Rates in China: A Discrete Choice Model Approach," Auburn Economics Working Paper Series auwp2014-12, Department of Economics, Auburn University.
    2. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    3. Kim, Hyeongwoo & Shi, Wen, 2018. "The determinants of the benchmark interest rates in China," Journal of Policy Modeling, Elsevier, vol. 40(2), pages 395-417.
    4. repec:zbw:bofitp:2019_008 is not listed on IDEAS
    5. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    6. Michael Funke & Andrew Tsang, 2021. "The Direction and Intensity of China’s Monetary Policy: A Dynamic Factor Modelling Approach," The Economic Record, The Economic Society of Australia, vol. 97(316), pages 100-122, March.
    7. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España.
    8. Funke, Michael & Tsang, Andrew, 2019. "The direction and intensity of China's monetary policy conduct: A dynamic factor modelling approach," BOFIT Discussion Papers 8/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
    9. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    10. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    11. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    12. Martin, Gael M. & Loaiza-Maya, Rubén & Maneesoonthorn, Worapree & Frazier, David T. & Ramírez-Hassan, Andrés, 2022. "Optimal probabilistic forecasts: When do they work?," International Journal of Forecasting, Elsevier, vol. 38(1), pages 384-406.
    13. repec:syb:wpbsba:01/2013 is not listed on IDEAS
    14. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2014. "Improving Density Forecasts and Value-at-Risk Estimates by Combining Densities," Tinbergen Institute Discussion Papers 14-090/III, Tinbergen Institute.
    15. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    16. Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
    17. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    18. Bjørnland, Hilde C. & Ravazzolo, Francesco & Thorsrud, Leif Anders, 2017. "Forecasting GDP with global components: This time is different," International Journal of Forecasting, Elsevier, vol. 33(1), pages 153-173.
    19. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    20. Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume, 2022. "High-frequency monitoring of growth at risk," International Journal of Forecasting, Elsevier, vol. 38(2), pages 582-595.
    21. McAdam, Peter & Warne, Anders, 2020. "Density forecast combinations: the real-time dimension," Working Paper Series 2378, European Central Bank.
    22. Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2019. "Higher Moment Constraints for Predictive Density Combinations," Working Papers BAWP-2019-01, University of Sydney Business School, Discipline of Business Analytics.

    More about this item

    Keywords

    Monetary policy indicators; China; forecast combination; optimal weights;
    All these keywords.

    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:syb:wpbsba:2123/20406. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Artem Prokhorov (email available below). General contact details of provider: https://edirc.repec.org/data/sbsydau.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.