A temporal cross section specification of the demand for gasoline using a random coefficient regression model

John Kraft and Mark Rodekohr

Energy, 1980, vol. 5, issue 12, 1193-1202

Abstract: This paper uses a random coefficient regression approach to estimate the demand for gasoline by pooling cross-sectional (state level) and time series data. The analysis proceeds by estimating two alternative models, namely a stock adjustment model and a flow adjustment model. The two models are estimated using state level data on gasoline consumption, gasoline price, income and the stock of automobiles. The random coefficient specification of each demand model is estimated assuming heteroskedastic disturbances across states, autocorrelated disturbances over time and variable intercept and slope coefficients across states. The resultant price and income elasticities are compared and inferences concerning the ability of the flow adjustment model to approximate the underlying demand function are made.

Date: 1980
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:5:y:1980:i:12:p:1193-1202

DOI: 10.1016/0360-5442(80)90061-4

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