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

Abstract

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 two-stage, 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.

Real effects of government debt on sustainable economic growth in Malaysia

“…This study utilizes the Autoregressive Distributed Lag (ARDL) co-integration tests. The routine cointegration approach at first utilized in this study depends on the ARDL model (Pesaran and Shin, 1999;Pesaran et al, 2001), which performs better to determine co-integration connections in small samples (Romilly et. al., 2001).…”

Sustainability of the Malawian current account deficit: Application of structural and solvency approaches

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An empirical analysis of energy cost pass-through to CO 2 emission prices

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