Prediction-driven approaches to discrete choicemodels with application to forecasting car typedemand

Sammanfattning: Models that can predict consumer choices are essential technical support fordecision makers in many contexts. The focus of this thesis is to address predictionproblems in discrete choice models and to develop methods to increase the predictivepower of these models with application to car type choice. In this thesis we challengethe common practice of prediction that is using statistical inference to estimateand select the ‘best’ model and project the results to a future situation. We showthat while the inference approaches are powerful explanatory tools in validating theexisting theories, their restrictive theory-driven assumptions make them not tailormadefor predictions. We further explore how modeling considerations for inferenceand prediction are different.Different papers of this thesis present various aspects of the prediction problemand suggest approaches and solutions to each of them.In paper 1, the problem of aggregation over alternatives, and its effects on bothestimation and prediction, is discussed. The focus of paper 2 is the model selectionfor the purpose of improving the predictive power of discrete choice models. Inpaper 3, the problem of consistency when using disaggregate logit models for anaggregate prediction question is discussed, and a model combination is proposedas tool. In paper 4, an updated version of the Swedish car fleet model is appliedto assess a Bonus-Malus policy package. Finally, in the last paper, we present thereal world applications of the Swedish car fleet model where the sensitivity of logitmodels to the specification of choice set affects prediction accuracy.