Allowing for intra-respondent variations in coefficients estimated on repeated choice data

By Stephane Hess, John M. Rose. 2009

116 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/5b73fe2a500973510c78c5e9fbd5d0ca36419829

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Citations in the corpus

Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis (2019, methodology): “Such models have been previously estimated by Hess and Train (2011), and Hess and Rose (2009) using a maximum simulated likelihood estimator.”
Sequential and Simultaneous Estimation of Hybrid Discrete Choice Models (2010, methodology): “If one would also like to accommodate random parameters, the log likelihood function would be even more complex and demanding in terms of estimation time, since the dimension of the multiple integrals to be evaluated would increase, as in the generalized panel model approach developed by Hess and Ro”
Latent class structures: taste heterogeneity and beyond (2014, background): “…in recent years been growing interest in allowing for intra-agent heterogeneity in addition to inter-agent heterogeneity (Bhat and Sardesai, 2006; Hess and Rose, 2009) making use of a specification such as: Ln (Ωγ ,Ωα) = ∫ α Tn∏ t=1 [∫ γ Pni∗t (β = α+ γ) f (γ | Ωγ) dγ ] h (α | Ωα) dα, (24) where…”
The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models (2011, methodology): “INTRODUCTION The “workhorse” multinomial logit model, used extensively in practice for econometric discrete choice analysis, was introduced by Luce and Suppes (1965) and McFadden (1974), and has a very simple and elegant structure. However, it is also saddled with the familiar independence from irre”
A simulation evaluation of the maximum approximate composite marginal likelihood (MACML) estimator for mixed multinomial probit models (2011, background): “…when the random coefficients have both an intra-individual and inter-individual random component (see Bhat and Castelar, 2002; Bhat and Sardesai, 2006, Hess and Rose, 2009), the utility structure may be written as: , qitqitU ε+′= qitqt xβ (5) where qtqqt βββ ~ += , Ω),(~ bβq N , )Ω ~,0(~~ Nqtβ .”
On the Treatment of Repeated Observations in Panel Data: Efficiency of Mixed Logit Parameter Estimates (2011, methodology): “We also tested a generalised approach suggested by Hess and Rose (2009) , which relaxes the assumption of intra-respondent homogeneneity of tastes and where the choice probability is given by:”