IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v43y2024i3p702-753.html
   My bibliography  Save this article

Forecasting CPI with multisource data: The value of media and internet information

Author

Listed:
  • Tingguo Zheng
  • Xinyue Fan
  • Wei Jin
  • Kuangnan Fang

Abstract

Using a large Chinese news corpus and Internet search data, we conduct an in‐depth out‐of‐sample forecasting study of Consumer Price Index (CPI) with the monthly macroeconomic database. For this purpose, we combine penalized regression and mixed‐frequency data sampling (MIDAS) methods to deal with the mixed‐frequency and high‐dimensional problems. Then we measure the time‐varying contributions of data from different sources and industries via dynamic model averaging. The results show that different types of data all provide enhancement in CPI forecasting, and the inclusion of media and Internet data further improves CPI forecasts' accuracy and timeliness due to their thorough coverage and fast update. In particular, the importance of alternative data is highlighted when the economy experiencing downturn or high uncertainty.

Suggested Citation

  • Tingguo Zheng & Xinyue Fan & Wei Jin & Kuangnan Fang, 2024. "Forecasting CPI with multisource data: The value of media and internet information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 702-753, April.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:3:p:702-753
    DOI: 10.1002/for.3048
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3048
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.3048?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
    ---><---

    References listed on IDEAS

    as
    1. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    2. 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).
    3. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    4. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    6. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    7. Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020. "A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth," International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.
    8. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    9. Leif Anders Thorsrud, 2020. "Words are the New Numbers: A Newsy Coincident Index of the Business Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
    10. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    11. Matteo Luciani & Madhavi Pundit & Arief Ramayandi & Giovanni Veronese, 2018. "Nowcasting Indonesia," Empirical Economics, Springer, vol. 55(2), pages 597-619, September.
    12. Paul Krugman, 1986. "Pricing to Market when the Exchange Rate Changes," NBER Working Papers 1926, National Bureau of Economic Research, Inc.
    13. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
    14. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    15. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    16. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    17. Paolo Fornaro & Henri Luomaranta, 2020. "Nowcasting Finnish real economic activity: a machine learning approach," Empirical Economics, Springer, vol. 58(1), pages 55-71, January.
    18. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    19. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    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. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    2. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    3. Erik Christian Montes Schütte, 2018. "In Search of a Job: Forecasting Employment Growth in the US using Google Trends," CREATES Research Papers 2018-25, Department of Economics and Business Economics, Aarhus University.
    4. Bonnier, Jean-Baptiste, 2022. "Forecasting crude oil volatility with exogenous predictors: As good as it GETS?," Energy Economics, Elsevier, vol. 111(C).
    5. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    6. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    7. 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.
    8. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    9. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    10. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    11. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
    12. Ching Hsu & Tina Yu & Shu-Heng Chen, 2021. "Narrative economics using textual analysis of newspaper data: new insights into the U.S. Silver Purchase Act and Chinese price level in 1928–1936," Journal of Computational Social Science, Springer, vol. 4(2), pages 761-785, November.
    13. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore & Wing-Keung Wong, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    14. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
    15. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
    16. Daniel Borup & Erik Christian Montes Schütte, 2022. "In Search of a Job: Forecasting Employment Growth Using Google Trends," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 186-200, January.
    17. Ashton de Silva & Maria Yanotti & Sarah Sinclair & Sveta Angelopoulos, 2023. "Place‐Based Policies and Nowcasting," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 56(3), pages 363-370, September.
    18. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
    19. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    20. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.

    More about this item

    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:wly:jforec:v:43:y:2024:i:3:p:702-753. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.