IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/52294.html
   My bibliography  Save this paper

A comparative assessment of aggregate car ownership model estimation methodologies

Author

Listed:
  • Sambracos, Evangelos
  • Paravantis, John

Abstract

This work examines the implications of advances in time series analysis on car ownership modeling in Greece. Variables include adults population ratio, GDP per capita, car occupancy, bus kilometers, inflation and unemployment. We developed and compared (a) a classical regression model estimated on raw levels, (b) an econometric model estimated on data stationarized using graphical and unit root tests and (c) an "atheoretical" ARIMA model. Although significant methodological implications were noted, all models forecast 48 to 49 private cars per 100 inhabitants by the year 2010, a development of momentous energy and environmental implications.

Suggested Citation

  • Sambracos, Evangelos & Paravantis, John, 2006. "A comparative assessment of aggregate car ownership model estimation methodologies," MPRA Paper 52294, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:52294
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/52294/1/MPRA_paper_52294.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Giovanni Baiocchi & Walter Distaso, 2003. "GRETL: Econometric software for the GNU generation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 105-110.
    2. Dermot Gately, 1990. "The U.S. Demand for Highway Travel and Motor Fuel," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 59-74.
    3. Hendry, David F & Mizon, Grayham E, 1978. "Serial Correlation as a Convenient Simplification, not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England," Economic Journal, Royal Economic Society, vol. 88(351), pages 549-563, September.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    car ownership; aggregate models; regression; time series analysis; forecasting.;

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L92 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Railroads and Other Surface Transportation
    • L99 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Other

    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:pra:mprapa:52294. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.