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Prediction-driven approaches to discrete choicemodels with application to forecasting car typedemand
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.ORCID iD: 0000-0002-6839-8540
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. , xv, 27 p.
Series
TRITA-TSC-PHD, 16:002
National Category
Social Sciences
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-180347ISBN: 978-91-87353-82-6 (print)OAI: oai:DiVA.org:kth-180347DiVA: diva2:893049
Public defence
2016-02-03, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 20160115

Available from: 2016-01-15 Created: 2016-01-11 Last updated: 2016-01-15Bibliographically approved
List of papers
1. Aggregation of alternatives and its influence on prediction
Open this publication in new window or tab >>Aggregation of alternatives and its influence on prediction
(English)Manuscript (preprint) (Other academic)
Abstract [en]

In car type choice models, alternatives are usually grouped into categories by some of their main characteristics such as make, model, vintage, body type and/or fuel type. Each of these categories contains di erent versions of the cars that are usually not recognized in the applied literature. In this study we empirically investigate whether including the heterogeneity of these versions in the modeling does matter in estimation and prediction or not. We use detailed data on alternatives available on the market down to the versions level of each model, which enables us to account for heterogeneity in the model. We also have Swedish car registry data to represent demand. We estimate separate discrete choice models with diferent methods of correction for alternative aggregation, including nesting structure. These models are estimated based on year 2006 Swedish registry data for new cars, and predict for 2007. The results show that including heterogeneity of cars' versions in the model improves model tness but it does not necessarily improve prediction results.

 

Keyword
Aggregate alternatives, prediction, car type choice, discrete choice modeling, clean vehicles
National Category
Social Sciences
Identifiers
urn:nbn:se:kth:diva-180340 (URN)
Note

QS 2016

Available from: 2016-01-11 Created: 2016-01-11 Last updated: 2016-01-15Bibliographically approved
2. Prediction-driven approach to model selection using feature selection and nonrandom hold-out validation
Open this publication in new window or tab >>Prediction-driven approach to model selection using feature selection and nonrandom hold-out validation
(English)Manuscript (preprint) (Other academic)
Abstract [en]

In this paper, we address the forecast problem and propose a prediction drivenapproach to model selection. Furthermore, we investigate to what extent dierentprediction questions lead to dierent \best" models. Most of the studies in theeld, take an inference-driven approach to select the best model and project theresults to the future population. In contrast, we take a prediction-driven approachfor both selection criteria and validation sample. We use feature (variable) selectionand nonrandom hold-out validation to select the model with the highest predictiveperformance in an out-of-sample prediction manner. The application of interest iscar type choice modeling using the Swedish car eet data. We introduce two dier-ent types of model selection criteria: maximum likelihood which is the conventionalmethod of model selection, and root mean squared error of the prediction quantityof interest. We compare the best models obtained by dierent criterion functions.The results show that the \best" model for the purpose of prediction depends con-siderably on the prediction question of interest. Moreover, when the objective isto predict a sub-section of a population such as the total share of ethanol cars,maximizing log-likelihood is not the most accurate model selection criterion.

Keyword
model selection, nonrandom hold-out validation, feature selection, out of sample prediction, car type choice models
National Category
Social Sciences
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-180341 (URN)
Note

QS 2016

Available from: 2016-01-11 Created: 2016-01-11 Last updated: 2016-01-15Bibliographically approved
3. Model combination for capturing the inconsistency in the aggregate prediction
Open this publication in new window or tab >>Model combination for capturing the inconsistency in the aggregate prediction
(English)Manuscript (preprint) (Other academic)
Abstract [en]

What is the appropriate aggregation level for modeling when the purpose of modelingis aggregate prediction: is it to estimate a disaggregate model and aggregateindividual predictions or estimate an aggregate model for the aggregate prediction?There is no unique answer to this old question in the literature as well as no generalmethodology to address the problem. In this paper, we propose to tackle theaggregation problem by employing and developing model combination methods tocombine aggregate and disaggregate models. Dierent aspects of aggregation arecovered in this paper: aggregation over time, individuals and alternatives. We examinethe eect of aggregation on the prediction accuracy of a nested multinomiallogit (NMNL). The application of interest is to predict the monthly share of cleancars in the Swedish car eet. We investigate a situation wherein the large scalemodels are already estimated, and we are interested in improving their predictionperformance in a post-processing manner. We combine NMNL with a regressiontree to capture individual heterogeneity as well as a time-series model to capturedynamics of the market share of clean cars at the aggregate level. Models are combinedthrough a latent variable model and a Bayesian model averaging approach.We propose aggregate likelihood as the likelihood to be maximized for the modelselection and combination when the purpose of modeling is aggregate prediction.The results show the increase in the predictive power of combined models.

Keyword
model combination, aggregation, logit models, prediction, latent variable models, Bayesian model averaging, nite mixture model
National Category
Social Sciences
Identifiers
urn:nbn:se:kth:diva-180342 (URN)
Note

QS 2016

Available from: 2016-01-11 Created: 2016-01-11 Last updated: 2016-01-15Bibliographically approved
4. Evaluation of Bonus-Malus systems for reducing car fleet CO2 emissions in Sweden
Open this publication in new window or tab >>Evaluation of Bonus-Malus systems for reducing car fleet CO2 emissions in Sweden
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Early 2014, an official Swedish government investigation report (FFF-report) was releasedproposing a policy package to promote a Fossil Free Fleet in Sweden by 2050. One objective ofthis policy package is to design a Bonus-Malus system that pushes the Swedish fleet compositiontowards the EU objectives of the average CO2 emissions of 95 g/km for new cars by 2021. Theproposed scenarios address cars bought by private persons as well as by companies. These scenariosdiffer in designs for registration tax, vehicle circulation tax, clean car premiums, company carbenefits tax and fuel tax. We use the Swedish car fleet model system to predict the effects of theproposed scenarios on the Swedish car fleet composition. Also, we build a simple supply model topredict future supply.Our model results show that none of the three proposed scenarios is actually successful enoughto meet the Swedish average CO2 emissions target of 95 g/km in 2020. The average CO2 emissionsin two of these scenarios are actually higher than in the business as usual scenario. Relative toa business as usual scenario the number of ethanol and gas cars is reduced in the other scenarioswhich is a negative result in terms of fossil fuel independence. Also, the Bonus-Malus system givesa positive net result in terms of budget effects showing that car buyers choose to pay the malus for acar with higher emissions rather than to be attracted by the bonus of a car with lower emissions.

Keyword
Bonus-Malus, CO2 emission policies, car fleet modeling, vehicle supply model
National Category
Social Sciences
Identifiers
urn:nbn:se:kth:diva-180343 (URN)
Note

QS 2016

Available from: 2016-01-11 Created: 2016-01-11 Last updated: 2016-01-15Bibliographically approved
5. The Swedish Car Fleet Model: Evaluation of Recent Applications
Open this publication in new window or tab >>The Swedish Car Fleet Model: Evaluation of Recent Applications
(English)Manuscript (preprint) (Other academic)
National Category
Social Sciences
Identifiers
urn:nbn:se:kth:diva-180344 (URN)
Note

QS 2016

Available from: 2016-01-11 Created: 2016-01-11 Last updated: 2016-01-15Bibliographically approved

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