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An empirical study of predicting car type choice in Sweden using cross-validation and feature-selection

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    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.

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    File URL: http://www.transportportal.se/swopec/CTS2013-13.pdf
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    Bibliographic Info

    Paper provided by CTS - Centre for Transport Studies Stockholm (KTH and VTI) in its series Working papers in Transport Economics with number 2013:13.

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    Length: 27 pages
    Date of creation: 16 Sep 2013
    Date of revision: 23 Apr 2014
    Handle: RePEc:hhs:ctswps:2013_013

    Contact details of provider:
    Postal: Centrum för Transportstudier (CTS), Teknikringen 10, 100 44 Stockholm, Sweden
    Web page: http://www.kth.se/abe/om_skolan/organisation/centra/cts

    Related research

    Keywords: Hold-out sample; Out of sample prediction; Feature selection; Cross validation; Model selection; Car type choice; Discrete choice modeling; Clean vehicles;

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    1. Brownstone, David & Bunch, David S. & Golob, Thomas F., 1994. "A Demand Forecasting System for Clean-Fuel Vehicles," University of California Transportation Center, Working Papers qt79c3g7xv, University of California Transportation Center.
    2. Michael P. Keane & Kenneth I. Wolpin, 2007. "Exploring The Usefulness Of A Nonrandom Holdout Sample For Model Validation: Welfare Effects On Female Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1351-1378, November.
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