IDEAS home Printed from https://ideas.repec.org/p/zbw/bofitp/bdp2011_001.html
   My bibliography  Save this paper

Comparing China's GDP statistics with coincident indicators

Author

Listed:
  • Mehrotra, Aaron
  • Pääkkönen, Jenni

Abstract

We use factor analysis to summarize information from various macroeconomic indicators, effectively producing coincident indicators for the Chinese economy. We compare the dynamics of the estimated factors with GDP, and compare our factors with other published indicators for the Chinese economy. The indicator data match the GDP dynamics well and discrepancies are very short. The periods of discrepancies seem to correspond to shocks affecting the growth process as neither autoregressive models for GDP itself nor various coincident indicators are able to forecast them satisfactorily.

Suggested Citation

  • Mehrotra, Aaron & Pääkkönen, Jenni, 2011. "Comparing China's GDP statistics with coincident indicators," BOFIT Discussion Papers 1/2011, Bank of Finland Institute for Emerging Economies (BOFIT).
    • Handle: RePEc:zbw:bofitp:bdp2011_001
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/212689/1/bofit-dp2011-001.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Zheng, Jingping, 2001. "China's official statistics: Growing with full vitality," China Economic Review, Elsevier, vol. 12(4), pages 333-337.
    3. Carsten A. Holz, 2004. "China's Statistical System in Transition: Challenges, Data Problems, and Institutional Innovations," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 50(3), pages 381-409, September.
    4. Xu, Xianchun, 2008. "Some differences in GDP measurements between China's practice and 1993 SNA guideline," China Economic Review, Elsevier, vol. 19(3), pages 480-488, September.
    5. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    6. Alwyn Young, 2003. "Gold into Base Metals: Productivity Growth in the People's Republic of China during the Reform Period," Journal of Political Economy, University of Chicago Press, vol. 111(6), pages 1220-1261, December.
    7. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    8. Gregory Chow, 2006. "Are Chinese Official Statistics Reliable?," CESifo Economic Studies, CESifo Group, vol. 52(2), pages 396-414, June.
    9. Xinhua He, 2010. "Noteworthy Discrepancies in China's GDP Accounting," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 18(s1), pages 88-102.
    10. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    11. Aigner, Dennis J. & Hsiao, Cheng & Kapteyn, Arie & Wansbeek, Tom, 1984. "Latent variable models in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 23, pages 1321-1393, Elsevier.
    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. Numan Ülkü & Kexing Wu, 2023. "Stock Market's Response to Real Output Shocks in China: A VARwAL Estimation," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 31(5), pages 1-25, September.
    2. repec:zbw:bofitp:2015_002 is not listed on IDEAS
    3. Mayo, Robert, 2015. "Hidden Risk: Detecting Fraud in Chinese Banks’ Non-performing Loan Data," MPRA Paper 98435, University Library of Munich, Germany.
    4. Holz, Carsten A., 2014. "The quality of China's GDP statistics," China Economic Review, Elsevier, vol. 30(C), pages 309-338.
    5. Jinshan Zhu & Hui Yao & Yingkai Tang & Liyong Wang, 2015. "An econometric analysis of sub-national Clean Development Mechanism performance in China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 20(7), pages 1137-1153, October.
    6. Lyu, Changjiang & Wang, Kemin & Zhang, Frank & Zhang, Xin, 2018. "GDP management to meet or beat growth targets," Journal of Accounting and Economics, Elsevier, vol. 66(1), pages 318-338.
    7. Chen, Shuo & Qiao, Xue & Zhu, Zhitao, 2021. "Chasing or cheating? Theory and evidence on China's GDP manipulation," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 657-671.
    8. Holz, Carsten A, 2013. "Chinese statistics: classification systems and data sources," MPRA Paper 43869, University Library of Munich, Germany.
    9. Liu, Ping & James Hueng, C., 2017. "Measuring real business condition in China," China Economic Review, Elsevier, vol. 46(C), pages 261-274.
    10. Ma, Ben & Song, Guojun & Zhang, Lei & Sonnenfeld, David A., 2014. "Explaining sectoral discrepancies between national and provincial statistics in China," China Economic Review, Elsevier, vol. 30(C), pages 353-369.
    11. Pang, Ke & Siklos, Pierre L., 2016. "Macroeconomic consequences of the real-financial nexus: Imbalances and spillovers between China and the U.S," Journal of International Money and Finance, Elsevier, vol. 65(C), pages 195-212.
    12. Haoyang Zhao & Jian Xu & Xinteng Liu, 2017. "How to evaluate the reliability of regional input–output data? A case for China," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 6(1), pages 1-22, December.
    13. Zhang, Jin & Li, Pujiang & Zhao, Guochang, 2018. "Is power generation really the gold measure of the Chinese economy? A conceptual and empirical assessment," Energy Policy, Elsevier, vol. 121(C), pages 211-216.
    14. Pang, Ke & Siklos, Pierre L., 2016. "Macroeconomic consequences of the real-financial nexus: Imbalances and spillovers between China and the U.S," Journal of International Money and Finance, Elsevier, vol. 65(C), pages 195-212.
    15. Schröder, Michael, 2017. "Konjunkturindikatoren für China: Projektbericht für das Ministerium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 162730.
    16. Heiner Mikosch & Ying Zhang, 2014. "Forecasting Chinese GDP Growth with Mixed Frequency Data," KOF Working papers 14-359, KOF Swiss Economic Institute, ETH Zurich.

    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. repec:zbw:bofitp:2011_001 is not listed on IDEAS
    2. Mehrotra, Aaron & Pääkkönen, Jenni, 2011. "Comparing China's GDP statistics with coincident indicators," Journal of Comparative Economics, Elsevier, vol. 39(3), pages 406-411, September.
    3. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    4. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    5. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
    6. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    7. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    8. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    9. Mao Takongmo, Charles Olivier & Stevanovic, Dalibor, 2015. "Selection Of The Number Of Factors In Presence Of Structural Instability: A Monte Carlo Study," L'Actualité Economique, Société Canadienne de Science Economique, vol. 91(1-2), pages 177-233, Mars-Juin.
    10. Alvarez, Rocio & Camacho, Maximo & Perez-Quiros, Gabriel, 2016. "Aggregate versus disaggregate information in dynamic factor models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 680-694.
    11. repec:dau:papers:123456789/11663 is not listed on IDEAS
    12. 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.
    13. Simon Beyeler & Sylvia Kaufmann, 2021. "Reduced‐form factor augmented VAR—Exploiting sparsity to include meaningful factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 989-1012, November.
    14. Sung Hoon Choi & Donggyu Kim, 2022. "Large Volatility Matrix Analysis Using Global and National Factor Models," Papers 2208.12323, arXiv.org, revised Dec 2022.
    15. 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.
    16. Choi, Sung Hoon & Kim, Donggyu, 2023. "Large volatility matrix analysis using global and national factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
    17. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    18. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346, April.
    19. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
    20. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    21. Luciani, Matteo, 2014. "Forecasting with approximate dynamic factor models: The role of non-pervasive shocks," International Journal of Forecasting, Elsevier, vol. 30(1), pages 20-29.
    22. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.

    More about this item

    Keywords

    factor models; principal component; GDP; China;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • P2 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies

    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:zbw:bofitp:bdp2011_001. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/bofitfi.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.