Estimating Price Elasticities of Demand for Highway Transport Using High Frequency Data

Abstract

A price elasticity estimation methodology for highway transportation by cars and trucks is developed and implemented by the authors. High frequency (daily) time series data is used on the number of vehicles passing through Slovenian toll stations. Price elasticity estimation is enhanced through high frequency data use in two ways: first, there is an increase in hypothesis test power with number of observations, and second, highway transportation demand temporal microstructure can be analyzed. Toll and fuel price elasticity estimation has a dynamic ARMA model applied. Care is taken to eliminate sample double seasonality: traffic volume depends of month of the year and day of the week. That the Slovenian highway system could be much more self-financing than it currently is, that statistically significant toll price elasticities show economic theory prediction signs, and that most estimated toll price elasticities are statistically insignificant are shown in the study results.

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