Discrete Choice Methods with Simulation

By Florian Heiss. 2016

4,841 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

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Citations in the corpus

Continual Deep Learning by Functional Regularisation of Memorable Past (2020, background): “The encoding in K − 1 length vectors ignores the last category which then ensures identiﬁability (Train, 2009).”
Cupid’s Invisible Hand: Social Surplus and Identification in Matching Models (2015, neutral): “there exists a scalar function u (ε), integrable with respect to P , such that (u,U) are (3)Williams (1977) and Daly and Zachary (1978).”
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence (2015, result): “We also note that the models studied in the present paper as well as in the aforementioned works fall under the broader paradigm of random utility models (Thurstone, 1927; Train, 2009; Azari Soufiani et al., 2013).”
Wasserstein Distributional Robustness and Regularization in Statistical Learning (2017, methodology): “With proper scaling, we recover the nested logit model (see, e.g., (4.2) in (Train 2009)).”
Consumers' willingness to pay for renewable and nuclear energy: A comparative analysis between the US and Japan (2015, background): “…utility of respondent n choosing alternative energy service i in choice situation t. Assuming that parameter 𝛽𝑛 is distributed with density function 𝑓(𝛽𝑛) (Train 2003, Louviere et al., 2000), the model specification allows for repeat choices by each respondent, such that the…”
Using mixtures in econometric models: a brief review and some new results (2016, background): “For instance, Train (2003) discusses the popular choice of mixed logit specifications.”
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models (2019, methodology): “…introduced in the discrete-choice literature (Luce, 1959), where it is used as a generative model for decisionmaking under utility maximization (Train, 2002; Aguirregabiria & Mira, 2010), where the log probabilities may be assumed to follow some functional form, such as being linear in…”
Restricted Boltzmann machines modeling human choice (2014, methodology): “A majority of the prior work on the choice model is about the MLM and its variants such as the hierarchical MLM [5], the multinomial probit model [6], and, generally, random utility models [17].”
Alternative Modeling Approaches Used for Examining Automobile Ownership: A Comprehensive Review (2014, methodology): “Unlike the random-effects MNP model, the random-effects MNL model is not restricted to normal distributions and the simulation of its choice probabilities is computationally easier (Train, 2003).”
Investigation of factors influencing motorcyclist injury severity using random parameters logit model with heterogeneity in means and variances (2021, methodology): “To estimate the random parameter mixed logit model using Halton sequences, the simulated maximum likelihood approach is used [47].”
Switching from fossil fuel to renewables in residential heating systems: An empirical study of homeowners' decisions in Germany (2014, methodology): “(2) to (4) are adjusted accordingly (for a more detailed technical description, see e.g. Train, 2003).”
Public Preferences for Carbon Tax Attributes (2015, background): “In addition, the respondent’s perception of what the interviewer expects as answers may influence what the respondent says she will do (Train, 2009). the cost of the CT that individuals incur per month.”
Location strategies for agglomeration economies (2014, positive): “For example, Train (2003) shows that estimated model parameters are consistent when using only a subset of alternatives in the decision making process.”
To Merge, Sell, or Liquidate? Socioemotional Wealth, Family Control, and the Choice of Business Exit (2019, positive): “Specifically, we relied on a mixed logit model, which allows direct comparisons among multiple discrete choices (for a review of the method and mathematical notations, see McFadden & Train, 2000; Train, 2003).”
Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters (2014, methodology): “It is a standard result in discrete choice theory (see, e.g., Ben-Akiva and Lerman 1985 and Train 2003) that if we offer the assortment S to the customers, then a customer chooses product i 2 S with probability where we use V = ( V 1 , … , V n ) to denote the vector of mean utilities of the…”
Consumer preferences for electric vehicles: a literature review (2017, methodology): “Thus, some studies used nested logit models to relax the restriction of IIA (Train, 2003).”
Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force. (2016, background): “The multiplication of the likelihood value for the respondent by the function of the upper-level distribution essentially keeps estimates of individual-level preference weights consistent with the assumed distribution of the sample preferences and not overly far from the sample preference mean [55].”