IDEAS home Printed from https://ideas.repec.org/a/kap/openec/v27y2016i5d10.1007_s11079-016-9406-z.html
   My bibliography  Save this article

Chinese Divisia Monetary Index and GDP Nowcasting

Author

Listed:
  • William A. Barnett

    (University of Kansas
    Center for Financial Stability)

  • Biyan Tang

    (University of Kansas)

Abstract

Since China’s enactment of the Reform and Opening-Up policy in 1978, China has become one of the world’s fastest growing economies, with an annual GDP growth rate exceeding 10 % between 1978 and 2008. But in 2015, Chinese GDP grew at 7 %, the lowest rate in 5 years. Many corporations complain that the borrowing cost of capital is too high. This paper constructs Chinese Divisia monetary aggregates M1 and M2, and, for the first time, constructs the broader Chinese monetary aggregates, M3 and M4. Those broader aggregates have never before been constructed for China, either as simple-sum or Divisia. The results shed light on the current Chinese monetary situation and the increased borrowing cost of money. GDP data are published only quarterly and with a substantial lag, while many monetary and financial decisions are made at a higher frequency. GDP nowcasting can evaluate the current month’s GDP growth rate, given the available economic data up to the point at which the nowcasting is conducted. Therefore, nowcasting GDP has become an increasingly important task for central banks. This paper nowcasts Chinese monthly GDP growth rate using a dynamic factor model, incorporating as indicators the Divisia monetary aggregate indexes, Divisia M1 and M2 along with additional information from a large panel of other relevant time series data. The results show that Divisia monetary aggregates contain more indicator information than the simple sum aggregates, and thereby help the factor model produce the best available nowcasting results. In addition, our results demonstrate that China’s economy experienced a regime switch or structure break in 2012, which a Chow test confirmed the regime switch. Before and after the regime switch, the factor models performed differently. We conclude that different nowcasting models should be used during the two regimes.

Suggested Citation

  • William A. Barnett & Biyan Tang, 2016. "Chinese Divisia Monetary Index and GDP Nowcasting," Open Economies Review, Springer, vol. 27(5), pages 825-849, November.
  • Handle: RePEc:kap:openec:v:27:y:2016:i:5:d:10.1007_s11079-016-9406-z
    DOI: 10.1007/s11079-016-9406-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11079-016-9406-z
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11079-016-9406-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    2. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters, in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224, National Bureau of Economic Research, Inc.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. Ryadh M. Alkhareif & William A. Barnett, 2012. "Divisia Monetary Aggregates for the GCC Countries," International Symposia in Economic Theory and Econometrics, in: Recent Developments in Alternative Finance: Empirical Assessments and Economic Implications, pages 1-37, Emerald Group Publishing Limited.
    5. Danny Quah & Thomas J. Sargent, 1993. "A Dynamic Index Model for Large Cross Sections," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 285-310, National Bureau of Economic Research, Inc.
    6. 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.
    7. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    8. Katerina Arnostova & David Havrlant & Luboš Rùžièka & Peter Tóth, 2011. "Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(6), pages 566-583, December.
    9. Martin D. D. Evans, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    10. William Barnett & Jia Liu & Ryan Mattson & Jeff Noort, 2013. "The New CFS Divisia Monetary Aggregates: Design, Construction, and Data Sources," Open Economies Review, Springer, vol. 24(1), pages 101-124, February.
    11. Barnett, William A., 2012. "Getting it Wrong: How Faulty Monetary Statistics Undermine the Fed, the Financial System, and the Economy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262516888, December.
    12. William A. Barnett, 2000. "Economic Monetary Aggregates: An Application of Index Number and Aggregation Theory," Contributions to Economic Analysis, in: The Theory of Monetary Aggregation, pages 11-48, Emerald Group Publishing Limited.
    13. Periklis Gogas & Theophilos Papadimitriou & Elvira Takli, 2013. "Comparison of simple sum and Divisia monetary aggregates in GDP forecasting: a support vector machines approach," Economics Bulletin, AccessEcon, vol. 33(2), pages 1101-1115.
    14. Richard G. Anderson & Barry E. Jones & Travis D. Nesmith, 1996. "Monetary aggregation theory and statistical index numbers," Working Papers 1996-007, Federal Reserve Bank of St. Louis.
    15. Matheson, Troy D., 2010. "An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys," Economic Modelling, Elsevier, vol. 27(1), pages 304-314, January.
    16. Yu, Qiao & Tsui, Albert K., 2000. "Monetary services and money demand in China," China Economic Review, Elsevier, vol. 11(2), pages 134-148, December.
    17. Apostolos Serletis & Periklis Gogas, 2014. "Divisia Monetary Aggregates, the Great Ratios, and Classical Money Demand Functions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(1), pages 229-241, February.
    18. Belongia, Michael T. & Ireland, Peter N., 2014. "The Barnett critique after three decades: A New Keynesian analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 5-21.
    19. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    20. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 25-44, February.
    21. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    22. William A. Barnett, 2000. "The Optimal Level of Monetary Aggregation," Contributions to Economic Analysis, in: The Theory of Monetary Aggregation, pages 125-149, Emerald Group Publishing Limited.
    23. Richard G. Anderson, 2006. "Monetary base," Working Papers 2006-049, Federal Reserve Bank of St. Louis.
    24. William A. Barnett, 2000. "The User Cost of Money," Contributions to Economic Analysis, in: The Theory of Monetary Aggregation, pages 6-10, Emerald Group Publishing Limited.
    25. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    26. Apostolos Serletis & Khandokar Istiak, 2016. "Are the Responses of the U.S. Economy Asymmetric to Positive and Negative Money Supply Shocks?," Open Economies Review, Springer, vol. 27(2), pages 303-316, April.
    27. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    28. William Barnett & Barry E. Jones & Travis D. Nesmith, 2008. "Divisia Second Moments: An Application of Stochastic Index Number Theory," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 200803, University of Kansas, Department of Economics, revised Jul 2008.
    29. Richard G. Anderson & Barry E. Jones, 2011. "A comprehensive revision of the U.S. monetary services (divisia) indexes," Review, Federal Reserve Bank of St. Louis, vol. 93(Sep), pages 325-360.
    30. Apostolos Serletis & Sajjadur Rahman, 2015. "On the Output Effects of Monetary Variability," Open Economies Review, Springer, vol. 26(2), pages 225-236, April.
    31. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
    32. Serletis, Apostolos & Rahman, Sajjadur, 2013. "The Case For Divisia Money Targeting," Macroeconomic Dynamics, Cambridge University Press, vol. 17(8), pages 1638-1658, December.
    33. William A. Barnett & Marcelle Chauvet & Danilo Leiva-Leon, 2014. "Real-Time Nowcasting of Nominal GDP Under Structural Breaks," Staff Working Papers 14-39, Bank of Canada.
    34. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    35. 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.
    36. Barnett, William A. & Jones, Barry E. & Nesmith, Travis D., 2008. "Divisia Second Moments," MPRA Paper 9111, University Library of Munich, Germany.
    37. William A Barnett & Marcelle Chauvet, 2011. "Financial Aggregation and Index Number Theory," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 7580, Juni.
    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. Stankevich, Ivan, 2023. "Application of Markov-Switching MIDAS models to nowcasting of GDP and its components," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 122-143.
    2. El-Shagi, Makram & Tochkov, Kiril, 2022. "Divisia monetary aggregates for Russia: Money demand, GDP nowcasting and the price puzzle," Economic Systems, Elsevier, vol. 46(4).
    3. Zhan, Minghua & Wang, Lijun & Zhan, Shuwei & Lu, Yao, 2023. "Does digital finance change the stability of money demand function? Evidence from China," Journal of Asian Economics, Elsevier, vol. 88(C).
    4. William A. Barnett & Kun He & Jingtong He, 2022. "Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation," JRFM, MDPI, vol. 15(10), pages 1-17, October.
    5. El-Shagi, Makram & Tochkov, Kiril, 2022. "Shadow of the colossus: Euro area spillovers and monetary policy in Central and Eastern Europe," Journal of International Money and Finance, Elsevier, vol. 120(C).
    6. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    7. John Nana Francois & Ryan S Mattson, 2019. "Divisia Monetary Aggregates for Developing Economies: Some Theory," Economics Bulletin, AccessEcon, vol. 39(3), pages 2221-2227.
    8. Hong, Puah & Leong, Choi-Meng & Mansor, Shazali & Lau, Evan, 2018. "Revisiting Money Demand in Malaysia: Simple-Sum versus Divisia Monetary Aggregates," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(2), pages 267-278.

    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. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    2. 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.
    3. 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.
    4. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    5. Van Nieuwenhuyze, Christophe & Benk, Szilard & Rünstler, Gerhard & Cristadoro, Riccardo & Den Reijer, Ard & Jakaitiene, Audrone & Jelonek, Piotr & Rua, António & Ruth, Karsten & Barhoumi, Karim, 2008. "Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise," Occasional Paper Series 84, European Central Bank.
    6. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    7. 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.
    8. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    9. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    10. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    11. Matteo Luciani & Lorenzo Ricci, 2014. "Nowcasting Norway," International Journal of Central Banking, International Journal of Central Banking, vol. 10(4), pages 215-248, December.
    12. Knut Aastveit & Tørres Trovik, 2012. "Nowcasting norwegian GDP: the role of asset prices in a small open economy," Empirical Economics, Springer, vol. 42(1), pages 95-119, February.
    13. Martin Feldkircher & Florian Huber & Josef Schreiner & Marcel Tirpák & Peter Tóth & Julia Wörz, 2015. "Bridging the information gap: small-scale nowcasting models of GDP growth for selected CESEE countries," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 56-75.
    14. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    15. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    16. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    17. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    18. Martin Feldkircher & Florian Huber & Josef Schreiner & Julia Woerz & Marcel Tirpak & Peter Toth, 2015. "Small-scale nowcasting models of GDP for selected CESEE countries," Working and Discussion Papers WP 4/2015, Research Department, National Bank of Slovakia.
    19. William Barnett & Marcelle Chauvetz & Danilo Leiva-Leonx, 2014. "Real-Time Nowcasting Nominal GDP Under Structural Break," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201313, University of Kansas, Department of Economics, revised Feb 2014.
    20. Kihwan Kim & Norman Swanson, 2013. "Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets," Departmental Working Papers 201315, Rutgers University, Department of Economics.

    More about this item

    Keywords

    China; Divisia monetary index; Borrowing cost of money; Nowcasting; Real GDP growth rate; Dynamic factor model; Regime switch;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East

    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:kap:openec:v:27:y:2016:i:5:d:10.1007_s11079-016-9406-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.