Document Type

Article

Publication Date

10-7-2019

Abstract

The automotive industry plays a key role in the European economy. In this paper, we determine which macro and socio-economic indicators have significant predictive power on car registrations - a proxy to automotive sector performance - across European countries. Contrary to the current literature which mainly focuses on long-term forecasting, we built our models on the highly seasonal monthly data of a medium-term period to make short-term forecasts. Our approach utilises predictors identified by the literature review. Presented models are built on the Vector Autoregressive models and are accompanied by formal tests, such as the Granger causality test. We have found mixed evidence about the importance of selected predictors as no general patterns were identified. We have found that the most useful predictor is the total number of registrations from the strongest export partner and past registration figures in the analysed country. Car registrations are virtually inelastic to the change of public transportation costs, fuel prices and short-term interest rates offered to households in most of the analysed countries. We have received mixed results about household sentiment across countries. Countries with a higher level of GDP are more sensitive to the changes in unemployment.

Comments

NOTICE: this is the author’s version of a work that was accepted for publication in Research in Transportation Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Research in Transportation Economics, volume 80, in 2020. https://doi.org/10.1016/j.retrec.2019.100752

The Creative Commons license below applies only to this version of the article.

Peer Reviewed

1

Copyright

Elsevier

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Thursday, October 07, 2021

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