Arbeitspapier

Semiparametric multinomial logit models for analysing consumer choice behaviour

The multinomial logit model (MNL) is one of the most frequently used statistical models in marketing applications. It allows to relate an unordered categorical response variable, for example representing the choice of a brand, to a vector of covariates such as the price of the brand or variables characterising the consumer. In its classical form, all covariates enter in strictly parametric, linear form into the utility function of the MNL model. In this paper, we introduce semiparametric extensions, where smooth effects of continuous covariates are modelled by penalised splines. A mixed model representation of these penalised splines is employed to obtain estimates of the corresponding smoothing parameters, leading to a fully automated estimation procedure. To validate semiparametric models against parametric models, we utilise proper scoring rules and compare parametric and semiparametric approaches for a number of brand choice data sets.

Sprache
Englisch

Erschienen in
Series: Discussion Paper ; No. 501

Thema
mixed models
multinomial logit model
brand choice
penalised splines
proper scoring rules
semiparametric regression
Markenartikel
Konsumentenverhalten
Logit-Modell
Nichtparametrisches Verfahren
Konsumtheorie

Ereignis
Geistige Schöpfung
(wer)
Kneib, Thomas
Baumgartner, Bernhard
Steiner, Winfried J.
Ereignis
Veröffentlichung
(wer)
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
(wo)
München
(wann)
2006

DOI
doi:10.5282/ubm/epub.1866
Handle
URN
urn:nbn:de:bvb:19-epub-1866-4
Letzte Aktualisierung
20.09.2024, 08:23 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Kneib, Thomas
  • Baumgartner, Bernhard
  • Steiner, Winfried J.
  • Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen

Entstanden

  • 2006

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