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Mixed-frequency Growth-at-Risk with the MIDAS-QR method: Evidence from China

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  • Xu, Qifa
  • Xu, Mengnan
  • Jiang, Cuixia
  • Fu, Weizhong

Abstract

High-frequency financial indicators provide more useful information and are efficient at forecasting low-frequency GDP. To this end, we extend the traditional Growth-at-Risk (GaR) framework for mixed frequency data. In this extension, monthly financial indicators are used to forecast quarterly GDP with the mixed data sampling-quantile regression (MIDAS-QR) method. Its ability for high-frequency monitoring of GaR is investigated using Chinese evidence. The evidence shows that our mixed-frequency GaR is promising in terms of good forecasting and nowcasting results, and can offer early warning of GDP downturns.

Suggested Citation

  • Xu, Qifa & Xu, Mengnan & Jiang, Cuixia & Fu, Weizhong, 2023. "Mixed-frequency Growth-at-Risk with the MIDAS-QR method: Evidence from China," Economic Systems, Elsevier, vol. 47(4).
  • Handle: RePEc:eee:ecosys:v:47:y:2023:i:4:s0939362523000651
    DOI: 10.1016/j.ecosys.2023.101131
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    1. 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.
    2. Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021. "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, vol. 101(C).
    3. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    4. Kwark, Noh-Sun & Lee, Changhyun, 2021. "Asymmetric effects of financial conditions on GDP growth in Korea: A quantile regression analysis," Economic Modelling, Elsevier, vol. 94(C), pages 351-369.
    5. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Chadwick, Meltem Gulenay & Ozturk, Huseyin, 2019. "Measuring financial systemic stress for Turkey: A search for the best composite indicator," Economic Systems, Elsevier, vol. 43(1), pages 151-172.
    8. Suarez, Javier, 2022. "Growth-at-risk and macroprudential policy design," Journal of Financial Stability, Elsevier, vol. 60(C).
    9. Scott Brave & R. Andrew Butters, 2012. "Diagnosing the Financial System: Financial Conditions and Financial Stress," International Journal of Central Banking, International Journal of Central Banking, vol. 8(2), pages 191-239, June.
    10. Anastasiya Ivanova & Alona Shmygel & Ihor Lubchuk, 2021. "The Growth-at-Risk (GaR) Framework: Implication For Ukraine," IHEID Working Papers 10-2021, Economics Section, The Graduate Institute of International Studies.
    11. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2014. "Nowcasting GDP in Real Time: A Density Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 48-68, January.
    12. Mr. Ananthakrishnan Prasad & Mr. Selim A Elekdag & Mr. Phakawa Jeasakul & Romain Lafarguette & Mr. Adrian Alter & Alan Xiaochen Feng & Changchun Wang, 2019. "Growth at Risk: Concept and Application in IMF Country Surveillance," IMF Working Papers 2019/036, International Monetary Fund.
    13. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    14. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    15. Brownlees, Christian & Souza, André B.M., 2021. "Backtesting global Growth-at-Risk," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 312-330.
    16. Mikkel Plagborg-Moller & Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020. "When Is Growth at Risk?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(1 (Spring), pages 167-229.
    17. Emanuele De Meo & Giacomo Tizzanini, 2021. "GDP‐network CoVaR: A tool for assessing growth‐at‐risk," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 50(2), July.
    18. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    19. Barnett, William A. & Chauvet, Marcelle & Leiva-Leon, Danilo, 2016. "Real-time nowcasting of nominal GDP with structural breaks," Journal of Econometrics, Elsevier, vol. 191(2), pages 312-324.
    20. Mr. Tobias Adrian & Mr. Francis Vitek, 2020. "Managing Macrofinancial Risk," IMF Working Papers 2020/151, International Monetary Fund.
    21. Eric Ghysels, 2014. "Conditional Skewness with Quantile Regression Models: SoFiE Presidential Address and a Tribute to Hal White," Journal of Financial Econometrics, Oxford University Press, vol. 12(4), pages 620-644.
    22. Wang, Bo & Li, Haoran, 2021. "Downside risk, financial conditions and systemic risk in China," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    23. Lima, Luiz Renato & Meng, Fanning & Godeiro, Lucas, 2020. "Quantile forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1149-1162.
    24. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
    25. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2021. "Systemic risk measures and distribution forecasting of macroeconomic shocks," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 178-196.
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