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Transportation Data as a Tool for Nowcasting Economic Activity – The German Road Pricing System as an Example

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  • Döhrn, Roland

Abstract

There is a broad agreement that transportation activity is closely linked to the business cycle. Nevertheless, data from the transportation sector have not been part of the tool kit of business cycle analysts due to long publications lags. With the disseminations of electronic road pricing systems, up to date figures on transportation activity are available for an increasing number of countries. This paper analyses the performance of the German toll statistics for nowcasting industry production. It confirms that between January 2007, when the toll data were published first, and July 2012 the seasonally adjusted toll data show a closer correlation with industry production than business surveys like the ifo business climate or the PMI. Compared to this the forecasting power out of sample is disappointing. Though showing somewhat smaller forecast errors than the alternative models tested the advantage of the toll based models is not statistically significant as a rule. Given the small publication lead against industry production and the publication lag against business sentiment indicators one should not be overenthusiastic on the opportunities of the toll data as a nowcasting tool, though they surely mean an addition to the business cycle analysts' tool box.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:rwirep:395
    DOI: 10.4419/86788450
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    References listed on IDEAS

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    1. Askitas, Nikos & Zimmermann, Klaus F., 2011. "The Toll Index: Innovation-based Economic Telemetry," IZA Policy Papers 31, Institute of Labor Economics (IZA).
    2. Gerhard Fenz & Martin Schneider, 2009. "A Leading Indicator of Austrian Exports Based on Truck Mileage," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 1, pages 44-52.
    3. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May.
    4. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    5. 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.
    6. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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    More about this item

    Keywords

    transportation data; nowcasting; forecasting performance;
    All these keywords.

    JEL classification:

    • 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

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