Working papers in Transport Economics
An empirical study of predicting car type choice in Sweden using cross-validation and feature-selection
(), Marcus Sundberg
() and Anders Karlström
Abstract: In this paper we analyze the prediction problem and focus
on building a multinomial logit model (MNL) to predict accurately, the
market shares of new cars in the Swedish car fleet in the short-term
future. Also, we investigate whether or not different prediction questions
lead to different 'best' models specifications. Most of the studies in the
field, take an inference-driven approach to select best models to estimate
relevant parameters and project the results to the future, whereas we do
take a prediction-driven approach. We use feature (variable) selection and
cross-validation algorithms to improve predictive performance of models.
These methods have been extensively used in other fields such as marketing
but are scarce studies employing them in the choice modeling field.
Additionally, we introduce four different prediction questions or
loss-functions: overall prediction (log-likelihood), brand market share,
ethanol (E85)/brand market share, and total share of ethanol cars and the
predicted results of these models are compared. The results show that
'best' models prediction depend different prediction questions to answer.
Also, they indicate that log-likelihood does not perform accurately when
the objective is to predict a sub-section of population such as total share
of E85 cars.
Keywords: Hold-out sample; Out of sample prediction; Feature selection; Cross validation; Model selection; Car type choice; Discrete choice modeling; Clean vehicles; (follow links to similar papers)
JEL-Codes: R41; (follow links to similar papers)
27 pages, September 16, 2013, Revised April 23, 2014
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