An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations of Heterogeneity

By Rick L. Andrews, Andrew Ainslie, Imran S. Currim. 2002

203 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/2387bfb7173937296be07155421d013c8b15a453

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Citations in the corpus

Evidence on the distribution of values of travel time savings from a six-week diary (2004, unknown): “It is not always true that the two methods give the same results. Ainslie et al. (2001) report”
Sales Promotion Models (2008, methodology): “While no one single method has been shown to be superior, and sometimes the differences across various approaches are not crucial (e.g., Andrews et al. 2002; Andrews et al. 2002 ), the general conclusion is that it is crucial to include some form of unobserved heterogeneity in the model, at a minim”
Latent class structures: taste heterogeneity and beyond (2014, methodology): “…as well as on the discussions in Greene and Hensher (2003), and complements a substantial body of empirical comparisons between the structures, for example in Andrews et al. (2002); Hanley et al. (2002); Greene and Hensher (2003); Scarpa et al. (2005); Provencher and Bishop (2004); Shen (2009).”
Heterogeneity distributions of willingness-to-pay in choice models (2007, methodology): “the LMD sometimes favors models with relatively poor parameter recovery ( Andrews et al. 2002; Liechty et al. 2005).”
Bricks or Clicks? Consumer Attitudes toward Traditional Stores and Online Stores (2013, background): “Whether a conjoint or self-explication approach is chosen (Srinivasan and Park 1997), or a logit choice model is estimated, heterogeneity must be recognized by allowing the preference coefficients to vary within the population (Andrews, Ansari, and Currim 2002, Andrews, Ainslie, and Currim 2002).”
An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs (2009, background): “We expect, however, that such an approach would allow an efficient estimation of mixed logit (Sándor and Wedel 2002) and latent class models (Andrews et al. 2002; Train 2003) that aim at modelling consumer heterogeneity.”
Discrete Choice Methods with Simulation by Kenneth E. Train (2003, methodology): “, Ainslie et al. (2001), the MSL and SMP estimates have differed.”
Endogeneity and marketing strategy research: an overview (2019, methodology): “While there was much discussion on which approach was preferable, Andrews et al. (2002) showed that both methods work equally well.”
Linking Marketing and Engineering Product Design Decisions via Analytical Target Cascading (2005, methodology): “Andrews et al. (2002b) provided a full discussion of these issues specifically in the context of conjoint.”
Enhancing Marketing with Engineering: Optimal Product Line Design for Heterogeneous Markets (2011, unknown): “Andrews, Ainslie, and Currim (2002); Andrews, Ansari, and Currim (2002) suggest that models with continuous (case i) and discrete (case ii) representations of heterogeneity recover parameter estimates and predict choices about equally well, except when the number of choices J is small, in which…”
Marketing Analytics for Data-Rich Environments (2016, unknown): “Although scholars initially debated which of these two approaches best represented heterogeneity, research has shown that the approaches each match speci ﬁ c types of marketing problems, with few differences between them (Andrews, Ainslie, and Currim 2002).”
Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach (2010, methodology): “the debate over continuous (RPL) versus discrete (LC) representation of preferences is still open within discrete choice literature (Wedel et al., 1999; Andrews et al., 2002 ).”