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A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.

A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.

A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.

A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.

A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.

A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.

A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.

A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.

A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.

A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-specific willingness to pay () measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs. This paper challenges this claim by deriving both population derived measures and individual-specific values based on the classical mixed logit model, establishing the extent of unacceptable valuations. Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward — indeed the individual-specific estimates are a by-product of the parameter estimation process. We also reveal the benefits of calculating measures from ratios of individual parameters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method appears overstated. Both approaches have merit.