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Nowcasting Business Cycles Using Toll Data

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  • Nikolaos Askitas
  • Klaus F. Zimmermann

Abstract

Nowcasting has been a challenge in the recent economic crisis. We introduce the Toll Index, a new monthly indicator for business cycle forecasting and demonstrate its relevance using German data. The index measures the monthly transportation activity performed by heavy transport vehicles across the country and has highly desirable availability properties (insignificant revisions, short publication lags) as a result of the innovative technology underlying its data collection. It is coincident with production activity due to the prevalence of just-in-time delivery. The Toll Index is a good early indicator of production as measured for instance by the German Production Index, provided by the German Statistical Office, which is a well-known leading indicator of the Gross National Product. The proposed new index is an excellent example of technological, innovation-driven economic telemetry, which we suggest should be established more around the world.
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Suggested Citation

  • Nikolaos Askitas & Klaus F. Zimmermann, 2013. "Nowcasting Business Cycles Using Toll Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 299-306, July.
    • Handle: RePEc:wly:jforec:v:32:y:2013:i:4:p:299-306
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    1. 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.
    2. Laurent Ferrara & Dominique Guégan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 186-199.
    3. 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.
    4. 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.
    5. 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.
    6. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo Group, vol. 56(2), pages 192-220, June.
    7. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    8. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    9. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Post-Print halshs-00460461, HAL.
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    Cited by:

    1. Roland Döhrn & Sönke Maatsch, 2012. "Der RWI/ISL-Containerumschlag-Index," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 92(5), pages 352-354, May.
    2. Roland Döhrn, 2013. "Transportation Data as a Tool for Nowcasting Economic Activity – The German Road Pricing System as an Example," Ruhr Economic Papers 0395, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    3. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    4. de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
    5. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    6. Jannsen, Nils, 2023. "Der Lkw-Maut-Fahrleistungsindex: Ein nützlicher Frühindikator für die Industrieproduktion," Kiel Insight 2023.02, Kiel Institute for the World Economy (IfW Kiel).
    7. repec:zbw:rwirep:0395 is not listed on IDEAS
    8. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    9. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2023. "Testing big data in a big crisis: Nowcasting under Covid-19," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1548-1563.
    10. Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy (IfW Kiel).
    11. Boysen-Hogrefe, Jens & Groll, Dominik & Hoffmann, Timo & Jannsen, Nils & Kooths, Stefan & Sonnenberg, Nils & Stamer, Vincent, 2023. "Deutsche Wirtschaft im Frühjahr 2023: Konjunktur fängt sich, Auftriebskräfte eher gering [German economy in spring 2023: Economy is stabilizing but little momentum going forward]," Kieler Konjunkturberichte 101, Kiel Institute for the World Economy (IfW Kiel).
    12. Selod,Harris & Soumahoro,Souleymane, 2020. "Big Data in Transportation : An Economics Perspective," Policy Research Working Paper Series 9308, The World Bank.
    13. Riccardo Corradini, 2019. "A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production," J, MDPI, vol. 2(4), pages 1-53, November.
    14. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    15. Döhrn, Roland, 2013. "Transportation Data as a Tool for Nowcasting Economic Activity – The German Road Pricing System as an Example," Ruhr Economic Papers 395, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    16. Indaco, Agustín, 2020. "From twitter to GDP: Estimating economic activity from social media," Regional Science and Urban Economics, Elsevier, vol. 85(C).

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    More about this item

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • 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
    • L92 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Railroads and Other Surface Transportation

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