IDEAS home Printed from https://ideas.repec.org/a/eee/inteco/v177y2024ics2110701723000744.html
   My bibliography  Save this article

Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends

Author

Listed:
  • Monge, Manuel
  • Claudio-Quiroga, Gloria
  • Poza, Carlos

Abstract

Since December 2019 we have been living with a virus called SARS-CoV-2 which has led to health policies being given prevalence over economic ones, causing serious consequences with regard to China's economic growth. For this purpose, we have built a Real Time Leading Economic Indicator based on Google Trends that improves the performance of Composite Leading Indicators (CLIs) to anticipate GDP trends and turning points for the Chinese economy. First, we assess the effectiveness of this new leading indicator relative to China's GDP by analyzing its statistical properties. We use fractional integration techniques to show the high degree of persistence of the new Real Time Leading Economic Indicator (RT-LEI) for China. Second, we observe the same relationship between GDP and RT-LEI in the long term using a Fractional Cointegration VAR (FCVAR) model. Third, we use a multivariate Continuous Wavelet Transform analysis to show which leading indicator best fits GDP and to identify when a structural change occurs. Finally, we forecast, using Artificial Neural Networks and a KNN model based on Machine Learning, our RT-LEI predicting the conclusion of a bearish scenario, after which the recovery begins in mid-2022.

Suggested Citation

  • Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
  • Handle: RePEc:eee:inteco:v:177:y:2024:i:c:s2110701723000744
    DOI: 10.1016/j.inteco.2023.100462
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2110701723000744
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.inteco.2023.100462?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. Søren Johansen & Morten Ørregaard Nielsen, 2012. "Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model," Econometrica, Econometric Society, vol. 80(6), pages 2667-2732, November.
    3. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
    4. Maggie E. C. Jones & Morten Ørregaard Nielsen & Michał Ksawery Popiel, 2014. "A fractionally cointegrated VAR analysis of economic voting and political support," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 47(4), pages 1078-1130, November.
    5. Melody Y. Huang & Randall R. Rojas & Patrick D. Convery, 2018. "News Sentiment as Leading Indicators for Recessions," Papers 1805.04160, arXiv.org, revised May 2018.
    6. 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.
    7. Dittmann, Ingolf & Granger, Clive W. J., 2002. "Properties of nonlinear transformations of fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 110(2), pages 113-133, October.
    8. Peter C.B. Phillips, 1987. "Multiple Regression with Integrated Time Series," Cowles Foundation Discussion Papers 852, Cowles Foundation for Research in Economics, Yale University.
    9. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    10. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    11. Narayan, Paresh Kumar & Narayan, Seema & Smyth, Russell, 2011. "Energy consumption at business cycle horizons: The case of the United States," Energy Economics, Elsevier, vol. 33(2), pages 161-167, March.
    12. Monokroussos, George & Zhao, Yongchen, 2020. "Nowcasting in real time using popularity priors," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1173-1180.
    13. Luís Aguiar-Conraria & Maria Soares, 2011. "Oil and the macroeconomy: using wavelets to analyze old issues," Empirical Economics, Springer, vol. 40(3), pages 645-655, May.
    14. Vacha, Lukas & Barunik, Jozef, 2012. "Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis," Energy Economics, Elsevier, vol. 34(1), pages 241-247.
    15. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    16. Tiwari, Aviral Kumar & Mutascu, Mihai Ioan & Albulescu, Claudiu Tiberiu, 2016. "Continuous wavelet transform and rolling correlation of European stock markets," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 237-256.
    17. Diebold, Francis X. & Rudebusch, Glenn D., 1991. "On the power of Dickey-Fuller tests against fractional alternatives," Economics Letters, Elsevier, vol. 35(2), pages 155-160, February.
    18. Aye, Goodness C. & Carcel, Hector & Gil-Alana, Luis A. & Gupta, Rangan, 2017. "Does gold act as a hedge against inflation in the UK? Evidence from a fractional cointegration approach over 1257 to 2016," Resources Policy, Elsevier, vol. 54(C), pages 53-57.
    19. Peter C.B. Phillips, 1999. "Discrete Fourier Transforms of Fractional Processes," Cowles Foundation Discussion Papers 1243, Cowles Foundation for Research in Economics, Yale University.
    20. Thoma, Mark, 2004. "Electrical energy usage over the business cycle," Energy Economics, Elsevier, vol. 26(3), pages 463-485, May.
    21. Johansen, Søren & Nielsen, Morten Ørregaard, 2010. "Likelihood inference for a nonstationary fractional autoregressive model," Journal of Econometrics, Elsevier, vol. 158(1), pages 51-66, September.
    22. Yang, Deli & Sonmez, Mahmut (Maho) & Li, Qinghai & Duan, Yibing, 2015. "The power of triple contexts on customer-based brand performance—A comparative study of Baidu and Google from Chinese netizens’ perspective," International Business Review, Elsevier, vol. 24(1), pages 11-22.
    23. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.
    24. Baillie, Richard T & Bollerslev, Tim, 1994. "Cointegration, Fractional Cointegration, and Exchange Rate Dynamics," Journal of Finance, American Finance Association, vol. 49(2), pages 737-745, June.
    25. Tanya Suhoy, 2009. "Query Indices and a 2008 Downturn: Israeli Data," Bank of Israel Working Papers 2009.06, Bank of Israel.
    26. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    27. Feng Guo & Ataman Ozyildirim & Victor Zarnowitz, 2009. "On the measurement and analysis of aggregate economic activity for China: the coincident economic indicators approach," China Economic Journal, Taylor & Francis Journals, vol. 2(2), pages 159-186.
    28. Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
    29. 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.
    30. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    31. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    32. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    33. Baruník, Jozef & Dvořáková, Sylvie, 2015. "An empirical model of fractionally cointegrated daily high and low stock market prices," Economic Modelling, Elsevier, vol. 45(C), pages 193-206.
    34. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    35. Sepideh Dolatabadi & Paresh Kumar Narayan & Morten Ørregaard Nielsen & Ke Xu, 2018. "Economic significance of commodity return forecasts from the fractionally cointegrated VAR model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 219-242, February.
    36. Jammazi, Rania & Ferrer, Román & Jareño, Francisco & Shahzad, Syed Jawad Hussain, 2017. "Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective?," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 453-483.
    37. Zhi-Qiang Jiang & Wei-Xing Zhou, 2011. "Multifractal detrending moving average cross-correlation analysis," Papers 1103.2577, arXiv.org, revised Mar 2011.
    38. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    39. Patrick M. Crowley & David G. Mayes, 2009. "How fused is the euro area core?: An evaluation of growth cycle co-movement and synchronization using wavelet analysis," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2008(1), pages 63-95.
    40. Dewandaru, Ginanjar & Masih, Rumi & Masih, A. Mansur M., 2016. "Contagion and interdependence across Asia-Pacific equity markets: An analysis based on multi-horizon discrete and continuous wavelet transformations," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 363-377.
    41. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    42. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    43. James E. Turley, 1976. "Automobile sales in perspective," Review, Federal Reserve Bank of St. Louis, vol. 58(Jun), pages 11-16.
    44. Phillips, Peter, 1999. "Discrete Fourier Transforms of Fractional Processes August," Working Papers 149, Department of Economics, The University of Auckland.
    45. Arturo Estrella & Mary R. Trubin, 2006. "The yield curve as a leading indicator: some practical issues," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 12(Jul).
    46. Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher Ré & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
    47. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    48. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    49. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
    50. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    51. Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
    52. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    53. Hibon, Michele & Evgeniou, Theodoros, 2005. "To combine or not to combine: selecting among forecasts and their combinations," International Journal of Forecasting, Elsevier, vol. 21(1), pages 15-24.
    54. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    55. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    56. Juan Carlos Berganza & Alberto Fuertes, 2018. "El aplanamiento de la curva de rendimientos en Estados Unidos," Boletín Económico, Banco de España, issue MAR.
    57. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    58. Johansen, SØren, 2008. "A Representation Theory For A Class Of Vector Autoregressive Models For Fractional Processes," Econometric Theory, Cambridge University Press, vol. 24(3), pages 651-676, June.
    59. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    60. Liwen Vaughan & Yue Chen, 2015. "Data mining from web search queries: A comparison of google trends and baidu index," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(1), pages 13-22, January.
    61. Michael D. Bauer & Thomas M. Mertens, 2018. "Economic Forecasts with the Yield Curve," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    62. Gao-Feng Gu & Wei-Xing Zhou, 2010. "Detrending moving average algorithm for multifractals," Papers 1005.0877, arXiv.org, revised Jun 2010.
    63. Aguiar-Conraria, Luís & Azevedo, Nuno & Soares, Maria Joana, 2008. "Using wavelets to decompose the time–frequency effects of monetary policy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2863-2878.
    64. Carlos Poza & Manuel Monge, 2020. "A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis," International Economics, CEPII research center, issue 163, pages 163-175.
    65. Wei-Xing Zhou, 2008. "Multifractal detrended cross-correlation analysis for two nonstationary signals," Papers 0803.2773, arXiv.org.
    66. Phillips, Peter C.B., 2007. "Unit root log periodogram regression," Journal of Econometrics, Elsevier, vol. 138(1), pages 104-124, May.
    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. Monge, Manuel & Lazcano, Ana & Parada, José Luis, 2023. "Growth vs value investing: Persistence and time trend before and after COVID-19," Research in International Business and Finance, Elsevier, vol. 65(C).
    2. Monge, Manuel & Poza, Carlos & Borgia, Sofía, 2022. "A proposal of a suspicion of tax fraud indicator based on Google trends to foresee Spanish tax revenues," International Economics, Elsevier, vol. 169(C), pages 1-12.
    3. Monge, Manuel & Cristóbal, Enrique, 2021. "Terrorism and the behavior of oil production and prices in OPEC," Resources Policy, Elsevier, vol. 74(C).
    4. Monge, Manuel & Gil-Alana, Luis A., 2021. "Lithium industry and the U.S. crude oil prices. A fractional cointegration VAR and a Continuous Wavelet Transform analysis," Resources Policy, Elsevier, vol. 72(C).
    5. Poza, Carlos & Monge, Manuel, 2020. "A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis," International Economics, Elsevier, vol. 163(C), pages 163-175.
    6. Monge, Manuel & Romero Rojo, María Fátima & Gil-Alana, Luis Alberiko, 2023. "The impact of geopolitical risk on the behavior of oil prices and freight rates," Energy, Elsevier, vol. 269(C).
    7. Monge, Manuel & Gil-Alana, Luis Alberiko, 2021. "Spatial crude oil production divergence and crude oil price behaviour in the United States," Energy, Elsevier, vol. 232(C).
    8. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    9. Benedikt Maas, 2020. "Short‐term forecasting of the US unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 394-411, April.
    10. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    11. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    12. Gil-Alana, Luis A. & Carcel, Hector, 2020. "A fractional cointegration var analysis of exchange rate dynamics," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    13. Berta Marcos Ceron & Manuel Monge, 2023. "Consumer Sentiment and Luxury Behavior in the United States before and after COVID-19: Time Trends and Persistence Analysis," Mathematics, MDPI, vol. 11(16), pages 1-14, August.
    14. Javier Hualde & Morten {O}rregaard Nielsen, 2022. "Fractional integration and cointegration," Papers 2211.10235, arXiv.org.
    15. Berta Marcos Ceron & Manuel Monge, 2024. "Luxury goods and services in recession periods. Time trends and persistence analysis," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(6), pages 588-595, December.
    16. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    17. Manuel Monge, 2024. "Trends and persistence in global olive oil prices after COVID-19," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(5), pages 481-488, October.
    18. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Nicola Rubino & Inmaculada Vilchez, 2024. "Modelling Loans to Non-Financial Corporations in the Eurozone: A Long-Memory Approach," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 30(3), pages 231-254, August.
    19. Monge, Manuel, 2021. "U.S. historical initial jobless claims. Is it different with the coronavirus crisis? A fractional integration analysis," International Economics, Elsevier, vol. 167(C), pages 88-95.
    20. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.

    More about this item

    Keywords

    Leading economic indicators; Business cycle; Google trends; Fractional cointegration; Machine learning;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - 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:eee:inteco:v:177:y:2024:i:c:s2110701723000744. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/21107017 .

    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.