A meta-model of vehicle ownership choice parameters

This paper builds a meta-model of vehicle ownership choice parameters to predict how their values might vary across extended periods as a function of macroeconomic variables. Multinomial logit models of vehicle ownership are estimated from repeated cross-sectional data between 1971 and 1996 for larg...

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Bibliographic Details
Main Authors: Chingcuanco, Franco (Contributor), Miller, Eric J. (Author)
Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering (Contributor)
Format: Article
Language:English
Published: Springer US, 2016-07-14T18:21:23Z.
Subjects:
Online Access:Get fulltext
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100 1 0 |a Chingcuanco, Franco  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Civil and Environmental Engineering  |e contributor 
100 1 0 |a Chingcuanco, Franco  |e contributor 
700 1 0 |a Miller, Eric J.  |e author 
245 0 0 |a A meta-model of vehicle ownership choice parameters 
260 |b Springer US,   |c 2016-07-14T18:21:23Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/103604 
520 |a This paper builds a meta-model of vehicle ownership choice parameters to predict how their values might vary across extended periods as a function of macroeconomic variables. Multinomial logit models of vehicle ownership are estimated from repeated cross-sectional data between 1971 and 1996 for large urban centers in Ontario. Three specifications are tested: a varying constants (VC) model where the alternative specific constants are allowed to vary each year; a varying scales (VS) model where the scale parameter varies instead; and a varying scales and constants model. The estimated parameters are then regressed on macroeconomic variables (e.g., employment rate, gas prices, etc.). The regressions yield good fit and statistically significant results, suggesting that changes in the macroeconomic environment influence household decision making over time, and that macroeconomic information could potentially help predict how model parameters evolve. This implies that the common assumption of holding parameters constant across forecast horizons could potentially be relaxed. Furthermore, using a separate validation dataset, the predictive power of the VC and VS models outperform conventional approaches providing further evidence that pooling data from multiple periods could also produce more robust models. 
546 |a en 
655 7 |a Article 
773 |t Transportation