IDEAS home Printed from https://ideas.repec.org/a/fip/fednep/89899.html

Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression

Author

Listed:

Abstract

Official Chinese GDP growth rates have been remarkably smooth over the past decade, in contrast with alternative Chinese economic data. To better identify Chinese business cycles, we construct a sparse partial least squares (PLS) factor from a wide array of Chinese higher-frequency data, targeted toward variables that are highly correlated with important aspects of the Chinese economy. Our resulting alternative growth indicator clearly identifies Chinese business cycle fluctuations and it performs well both in out-of-sample testing for China as well as when applied to other economies. Using this indicator, we decompose deviations from growth trends into global growth, credit supply, and monetary policy components, and this decomposition suggests that, in contrast to China’s 2015-16 slowdown, the country’s 2018-19 slowdown was mainly due to deteriorating domestic credit conditions.

Suggested Citation

  • Jan J. J. Groen & Michael Nattinger, 2020. "Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression," Economic Policy Review, Federal Reserve Bank of New York, vol. 26(4), pages 39-68, October.
  • Handle: RePEc:fip:fednep:89899
    as

    Download full text from publisher

    File URL: https://www.newyorkfed.org/medialibrary/media/research/epr/2020/epr_2020_china-sparse-pls_groen.pdf
    File Function: Full text
    Download Restriction: no

    File URL: https://www.newyorkfed.org/research/epr/2020/epr_2020_china-sparse-pls_groen.html
    File Function: Summary
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hunter L. Clark & Jeffrey B. Dawson & Maxim L. Pinkovskiy, 2020. "How Stable Is China’s Growth? Shedding Light on Sparse Data," Economic Policy Review, Federal Reserve Bank of New York, vol. 26(4), pages 1-38, October.
    2. Krämer, Nicole & Sugiyama, Masashi, 2011. "The Degrees of Freedom of Partial Least Squares Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 697-705.
    3. Karel Mertens & Morten O. Ravn, 2013. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States," American Economic Review, American Economic Association, vol. 103(4), pages 1212-1247, June.
    4. 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.
    5. Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
    6. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    7. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
    8. Hyonho Chun & Sündüz Keleş, 2010. "Sparse partial least squares regression for simultaneous dimension reduction and variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 3-25, January.
    9. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-2009 Recession," NBER Working Papers 18094, National Bureau of Economic Research, Inc.
    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. William Barcelona & Danilo Cascaldi-Garcia & Jasper Hoek & Eva Van Leemput, 2022. "What Happens in China Does Not Stay in China," International Finance Discussion Papers 1360, Board of Governors of the Federal Reserve System (U.S.).

    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. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    2. Masud Alam, 2021. "Heterogeneous Responses to the U.S. Narrative Tax Changes: Evidence from the U.S. States," Papers 2107.13678, arXiv.org.
    3. Bae, Juhee, 2024. "Factor-augmented forecasting in big data," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1660-1688.
    4. Barbarino, Alessandro & Bura, Efstathia, 2024. "Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables," Econometrics and Statistics, Elsevier, vol. 31(C), pages 1-18.
    5. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    6. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Laumer, Sebastian, 2020. "Government spending and heterogeneous consumption dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    8. Masud Alam, 2021. "Output, Employment, and Price Effects of U.S. Narrative Tax Changes: A Factor-Augmented Vector Autoregression Approach," Papers 2106.10844, arXiv.org.
    9. Beltrán, Felipe & Coble, David, 2024. "Monetary policy surprises on the banking sector: The role of the information and pure monetary shocks," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(3).
    10. Eickmeier, Sandra & Metiu, Norbert & Prieto, Esteban, 2016. "Time-varying volatility, financial intermediation and monetary policy," Discussion Papers 46/2016, Deutsche Bundesbank.
    11. Georgiadis, Georgios & Schumann, Ben, 2021. "Dominant-currency pricing and the global output spillovers from US dollar appreciation," Journal of International Economics, Elsevier, vol. 133(C).
    12. Anh D. M. Nguyen & Luisanna Onnis & Raffaele Rossi, 2021. "The Macroeconomic Effects of Income and Consumption Tax Changes," American Economic Journal: Economic Policy, American Economic Association, vol. 13(2), pages 439-466, May.
    13. Anastasios Evgenidis & Apostolos Fasianos, 2025. "AI news shocks and the macroeconomy: evidence from UK patent data," IFS Working Papers W25/48, Institute for Fiscal Studies.
    14. Martin Bruns & Helmut Lütkepohl, 2023. "An Alternative Bootstrap for Proxy Vector Autoregressions," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1857-1882, December.
    15. Mirela Miescu & Haroon Mumtaz, 2019. "Proxy structural vector autoregressions, informational sufficiency and the role of monetary policy," Working Papers 280730188, Lancaster University Management School, Economics Department.
    16. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2021. "The real effects of financial uncertainty shocks: A daily identification approach," Working Papers 61, Red Nacional de Investigadores en Economía (RedNIE).
    17. Alexander Chudik & M. Hashem Pesaran, 2016. "Theory And Practice Of Gvar Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 165-197, February.
    18. Giancarlo Corsetti & Joao B Duarte & Samuel Mann, 2022. "One Money, Many Markets [Fixed Rate Versus Adjustable Rate Mortgages: Evidence from Euro Area Banks]," Journal of the European Economic Association, European Economic Association, vol. 20(1), pages 513-548.
    19. Michael Ryan, 2020. "A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty," Working Papers in Economics 20/10, University of Waikato.
    20. Maxime Leroux & Rachidi Kotchoni & Dalibor Stevanovic, 2017. "Forecasting economic activity in data-rich environment," Working Papers hal-04141668, HAL.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • F01 - International Economics - - General - - - Global Outlook
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

    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:fednep:89899. 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: Gabriella Bucciarelli (email available below). General contact details of provider: https://edirc.repec.org/data/frbnyus.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.