IDEAS home Printed from https://ideas.repec.org/p/zbw/ifwbox/202302.html

Der Lkw-Maut-Fahrleistungsindex: Ein nützlicher Frühindikator für die Industrieproduktion

Author

Listed:
  • Jannsen, Nils

Abstract

Der Lkw-Fahrleistungsindex basiert auf der Mauterhebung, bei der der Lkw-Verkehr auf allen Bundesstraßen und Autobahnen erfasst wird. Da rund 80 Prozent der Warenbeförderung per Lkw erfolgt, steht der Fahrleistungsindex in enger Verbindung mit der wirtschaftlichen Aktivität, insbesondere mit der Industrieproduktion. Als Frühindikator ist die Fahrleistung zudem deshalb vielversprechend, da sie sehr frühzeitig nach Ende eines Berichtsmonats veröffentlicht wird. Die recht hohe Korrelation mit der Industrieproduktion (Ademmer et al. 2021: Kapitel 2.7.1; Askitas und Zimmermann 2013; Cox et al. 2018) spricht dafür, dass die Fahrleistung ein nützlicher Frühindikator ist. Die Prognosegüte des Indikators - auch im Vergleich zu anderen Frühindikatoren - wurde bislang jedoch kaum systematisch untersucht. Eine frühe Studie kommt zu dem Schluss, dass die Prognosegüte der Lkw-Fahrleistung nicht über die anderer Frühindikatoren, insbesondere Unternehmensbefragungen, hinausgeht (Döhrn 2011). Allerdings hat sich die Datenbasis seitdem deutlich verbreitert. Im Folgenden werden die Prognoseeigenschaften des monatlichen Lkw-Fahrleistungsindex für die Industrieproduktion untersucht.

Suggested Citation

  • 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).
    • Handle: RePEc:zbw:ifwbox:202302
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/271158/1/1840603054.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michele Lenza & Giorgio E. Primiceri, 2020. "How to Estimate a VAR after March 2020," NBER Working Papers 27771, National Bureau of Economic Research, Inc.
    2. 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.
    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. Boysen-Hogrefe, Jens & Groll, Dominik & Hoffmann, Timo & Jannsen, Nils & Kooths, Stefan & Sonnenberg, Nils & Stamer, Vincent, 2023. "Deutsche Wirtschaft im Sommer 2023: Konjunktur tastet sich aus der Krise [German Economy in Summer 2023: Crawling out of the crisis]," Kieler Konjunkturberichte 104, Kiel Institute for the World Economy (IfW Kiel).
    2. Boysen-Hogrefe, Jens & Groll, Dominik & Hoffmann, Timo & Jannsen, Nils & Kooths, Stefan & Sonnenberg, Nils & Stamer, Vincent, 2023. "Deutsche Wirtschaft im Winter 2023: Finanzpolitik in Turbulenzen - Gegenwind für die Erholung [German Economy in Winter 2023: Public budget under stress - Recovery faces headwinds]," Kieler Konjunkturberichte 110, Kiel Institute for the World Economy (IfW Kiel).

    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. 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).
    2. Miescu, Mirela & Rossi, Raffaele, 2021. "COVID-19-induced shocks and uncertainty," European Economic Review, Elsevier, vol. 139(C).
    3. Andersson, Malin & Maurin, Laurent & Rusinova, Desislava, 2021. "Market finance as a spare tyre? Corporate investment and access to bank credit in Europe," Working Paper Series 2606, European Central Bank.
    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. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2024. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Post-Print hal-05056934, HAL.
    6. Rybacki, Jakub & Gniazdowski, Michał, 2021. "Macroeconomic Forecasting in Poland: Lessons From the COVID-19 Outbreak," MPRA Paper 107682, University Library of Munich, Germany.
    7. Anton I. Votinov & Julia A. Polshchikova & Karen A. Nersisyan, 2025. "Macroeconomic Modeling in Post-pandemic Times," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 62-73, February.
    8. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2020. "Measuring Uncertainty and Its Effects in the COVID-19 Era," Working Papers 20-32R, Federal Reserve Bank of Cleveland, revised 05 Jan 2022.
    9. Thomas B Götz & Klemens Hauzenberger, 2021. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 442-461.
    10. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    11. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    12. Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2023. "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Journal of Econometrics, Elsevier, vol. 232(1), pages 52-69.
    13. Vito Polito & Yunyi Zhang, 2021. "Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression," CESifo Working Paper Series 9395, CESifo.
    14. Brand, Claus & Goy, Gavin W & Lemke, Wolfgang, 2020. "Natural rate chimera and bond pricing reality," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224546, Verein für Socialpolitik / German Economic Association.
    15. Jakub Rybacki & Michał Gniazdowski, 2023. "Macroeconomic forecasting in Poland: lessons from the external shocks," Bank i Kredyt, Narodowy Bank Polski, vol. 54(1), pages 45-64.
    16. Askitas Nikos & Martinez Anoop Bindra & Cereda Fabio Saia, 2025. "The IZA/Fable Swipe Consumption Index," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 245(4-5), pages 589-598.
    17. Andrejs Zlobins, 2021. "On the Time-varying Effects of the ECB's Asset Purchases," Working Papers 2021/02, Latvijas Banka.
    18. Montes-Galdón, Carlos & Paredes, Joan & Wolf, Elias, 2022. "Conditional density forecasting: a tempered importance sampling approach," Working Paper Series 2754, European Central Bank.
    19. Indaco, Agustín, 2020. "From twitter to GDP: Estimating economic activity from social media," Regional Science and Urban Economics, Elsevier, vol. 85(C).
    20. De Backer, Bruno & Dewachter, Hans & Iania, Leonardo, 2021. "Macrofinancial information on the post-COVID-19 economic recovery: Will it be V, U or L-shaped?," Finance Research Letters, Elsevier, vol. 43(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:ifwbox:202302. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/iwkiede.html .

    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.