Skip to main content

Car ownership and use in Britain: a comparison of the empirical results of alternative cointegration estimation methods and forecasts

Buy Article:

$47.00 plus tax (Refund Policy)

This paper addresses two problems faced by many forecasters in the transport sector, namely how to use a relatively small sample to forecast car ownership over a long period of time and avoid the difficulties caused by spurious or nonsense regressions. Five alternative estimation methods are used to test for cointegrating relationships between per capita car ownership (and use) and real per capita personable disposable income, real motoring costs and real bus fares. These are the Engle–Granger twostage, the Phillips-Hansen fully modified, the Wickens–Breusch one-stage, the autoregressive distributed lag, and the Johansen maximum likelihood methods. The corresponding error correction models are estimated, and a comparison made between the derived short- and long-run demand elasticities for car ownership and use. The ex-post forecasting performance of the error correction models, together with an ARIMA model specification, is evaluated using a number of performance criteria. The long-range time series forecasts obtained from the cointegrating regressions are compared with those from the cross-sectional approach used by the UK Department of the Environment, Transport and the Regions, and the policy implications discussed.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Publication date: 2001-11-15

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more