Nearest neighbour prediction method in mixed logit discrete choice model

• Ano de defesa: 2021
• Autor(a) principal: Tsagbey, Awo Sitsofe
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Discrete choice model; Efeitos aleatórios; Misto logito; Mixed logit; Modelo de escolha discreta; Nearest neighbour; Predição; Prediction; Random effects; Vizinho mais próximo
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/45/45133/tde-09092021-190423/

Discrete choice models are a group of models that are used to analyze choice data basically because they accommodate the nature of the process that generates the data. The most common types of discrete models include the logit, probit, multinomial logit, nested logit, mixed logit and most recently the generalized multinomial logit. Discrete choice models have been mostly used in the area economics, transportation, energy, psychology, etc. Prediction in these models isn\'t uncommon, in contexts such as engineering, marketing, and production, discrete choice models are mostly used to forecast demand. Unfortunately, for out-of-sample prediction at the individual level for complex models such as mixed logit, which involves predicting the random effects/parameters, there isn\'t any work found in literature. Thus, in this is work we propose a method for this scenario in mixed logit discrete models using the nearest neighbour concept. We carry out various simulations and then apply on two types of real-life data. We find that the prediction accuracy of this new method is better than the rudimentary method of using the population parameters especially when the model fitted isn\'t the very best.