Dynamic Assortment Planning Under Nested Logit Models

Author(s):  
Xi Chen ◽  
Chao Shi ◽  
Yining Wang ◽  
Yuan Zhou
1996 ◽  
Vol 50 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Joseph A Herriges ◽  
Catherine L Kling

1987 ◽  
Vol 19 (3) ◽  
pp. 395-402 ◽  
Author(s):  
J L Horowitz

The nested or sequential logit model is the only computationally tractable randomutility model that permits correlation among the random components of the utility functions of different alternatives. In this paper, two specification tests are described for nested logit models. One is a test of a maintained model against a nonnested alternative. This test can be used, among other purposes, to discriminate among models with different tree structures. It can be implemented by hand using the results of sequential estimation of the models under consideration. The other test consists of comparing the sequentially estimated parameter values with values produced by an asymptotically efficient estimation technique. This test does not require estimating an alternative to the maintained model.


Author(s):  
Kátia Andrade ◽  
Seiichi Kagaya

In Japan, cycling is a widely accepted transportation mode and often used for commuting or other purposes. Accordingly, this paper focuses on the reasons that motivate people to cycle, even though the Japanese transportation policies towards cycling are somewhat limited when compared to other countries with high cycling levels. Behavioural and statistical analyses are presented with a focus on unimodal commuting trips. In the behavioural analysis, commuters’ views on cycling are presented. In the statistical analysis, Nested Logit models are estimated to assess factors with strong influence on cycling. This paper contributes to further understanding the behaviour of active cyclists.


Author(s):  
Florian Heiss

The nested logit model has become an important tool for the empirical analysis of discrete outcomes. There is some confusion about its specification of the outcome probabilities. Two major variants show up in the literature. This paper compares both and finds that one of them (called random utility maximization nested logit, RUMNL) is preferable in most situations. Since the command nlogit of Stata 7.0 implements the other variant (called non-normalized nested logit, NNNL), an implementation of RUMNL called nlogitrum is introduced. Numerous examples support and illustrate the differences between both specifications.


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