Heterogeneous tastes across consumers can be captured by random coefficients in a mixed logit (ML) model. However, other types of factors that may not directly affect taste could cause choices to vary, such as choice context, choice task complexity, and demographic characters. This paper jointly considers taste heterogeneity around reference-dependent attributes and other choice variability through inclusion of a scale function, based on data from a stated preference experiment for bread. Results demonstrate that modeling other sources of choice variability in addition to taste heterogeneity increases the model fit, although the improvement is not dramatic
Analyses of data from random utility models of choice data have typically used fixed parameter repre...
When modeling demand for differentiated products, it is vital to adequately capture consumer taste h...
Partly as a result of the increasing reliance on Stated Choice (SC) data, the vast majority of discr...
Heterogeneous tastes across consumers can be captured by random coefficients in a mixed logit (ML) m...
Understanding and accommodating heterogeneity in variance (also referred to as heteroscedasticity) a...
While there is general agreement that consumer taste heterogeneity is crucially important in marketi...
There is growing interest in establishing a mechanism to account for scale heterogeneity across indi...
The structure of consumer taste heterogeneity in discrete choice demand models is important, as it d...
There is growing interest in establishing a mechanism to account for scale heterogeneity across indi...
Unobserved heterogeneity of error scale in choice models is a recent extension of the better investi...
Most applications of discrete choice models in transportation now utilise a random coefficient speci...
This paper discusses the differences between Multinomial Logit, Random Coefficients Mixed Logit and ...
Researchers using revealed preference data have mostly relied on the Mixed Logit (ML) framework to m...
Models to analyse discrete choice data that account for heterogeneity in error variance (scale) acro...
Reflecting growing interest from both consumers and policymakers and building on recent developments...
Analyses of data from random utility models of choice data have typically used fixed parameter repre...
When modeling demand for differentiated products, it is vital to adequately capture consumer taste h...
Partly as a result of the increasing reliance on Stated Choice (SC) data, the vast majority of discr...
Heterogeneous tastes across consumers can be captured by random coefficients in a mixed logit (ML) m...
Understanding and accommodating heterogeneity in variance (also referred to as heteroscedasticity) a...
While there is general agreement that consumer taste heterogeneity is crucially important in marketi...
There is growing interest in establishing a mechanism to account for scale heterogeneity across indi...
The structure of consumer taste heterogeneity in discrete choice demand models is important, as it d...
There is growing interest in establishing a mechanism to account for scale heterogeneity across indi...
Unobserved heterogeneity of error scale in choice models is a recent extension of the better investi...
Most applications of discrete choice models in transportation now utilise a random coefficient speci...
This paper discusses the differences between Multinomial Logit, Random Coefficients Mixed Logit and ...
Researchers using revealed preference data have mostly relied on the Mixed Logit (ML) framework to m...
Models to analyse discrete choice data that account for heterogeneity in error variance (scale) acro...
Reflecting growing interest from both consumers and policymakers and building on recent developments...
Analyses of data from random utility models of choice data have typically used fixed parameter repre...
When modeling demand for differentiated products, it is vital to adequately capture consumer taste h...
Partly as a result of the increasing reliance on Stated Choice (SC) data, the vast majority of discr...