Estimation bias in choice models with last choice feedback

Degeratu, Alexandru M.

doi:10.1016/s0167-8116(99)00019-1

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…The 1 Econometrically speaking, the RP choice variable is correlated with the error term in the SP choice equation in the presence of unobserved heterogeneity. This issue is similar to the initial conditions problem in the panel data literature (Chamberlain, 1980;Degeratu, 1999). 7 paper is structured as follows.…”

Section: A Unified Rp-sp Modeling Frameworkmentioning

confidence: 95%

A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area

Bhat

Castelar

2002

Transportation Research Part B: Methodological

186

105

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This paper formulates and applies a unified mixed-logit framework for joint analysis of revealed and stated preference data that accommodates a flexible competition pattern across alternatives, scale difference in the revealed and stated choice contexts, heterogeneity across individuals in the intrinsic preferences for alternatives, heterogeneity across individuals in the responsiveness to level-of-service factors, state dependence of the stated choices on the revealed choice, and heterogeneity across individuals in the state dependence effect. The estimation of the mixed logit formulation is achieved using simulation techniques that employ quasi-random Monte Carlo draws. The formulation is applied to examine the travel behavior responses of San Francisco Bay Bridge users to changes in travel conditions. The data for the study are drawn from surveys conducted as part of the 1996 San Francisco Bay Area Travel Study. The results of the mixed logit formulation are compared with those of more restrictive structures on the basis of parameter estimates, implied trade-offs among level-of-service attributes, heterogeneity and state dependence effects, data fit, and substantive implications of congestion pricing policy simulations.

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Section: A Unified Rp-sp Modeling Frameworkmentioning

confidence: 95%

A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area

Bhat

Castelar

2002

Transportation Research Part B: Methodological

186

105

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“…5 To the extent that heterogeneity in the regression coefficients exists, the state dependence effect will likely be overestimated (Degeratu 1999; Frank 1962). This would result in underestimation of the effects of the customer equity drivers, which means that the effect of violation of this assumption would be to make the projections of the model more conservative.…”

Section: Linking Marketing Actions To Financial Returnmentioning

confidence: 99%

Return on Marketing: Using Customer Equity to Focus Marketing Strategy

Rust

Lemon

Zeithaml

2004

Journal of Marketing

1,631

1,256

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The authors present a unified strategic framework that enables competing marketing strategy options to be traded off on the basis of projected financial return, which is operationalized as the change in a firm's customer equity relative to the incremental expenditure necessary to produce the change. The change in the firm's customer equity is the change in its current and future customers' lifetime values, summed across all customers in the industry. Each customer's lifetime value results from the frequency of category purchases, average quantity of purchase, and brand-switching patterns combined with the firm's contribution margin. The brand-switching matrix can be estimated from either longitudinal panel data or cross-sectional survey data, using a logit choice model. Firms can analyze drivers that have the greatest impact, compare the drivers' performance with that of competitors' drivers, and project return on investment from improvements in the drivers. To demonstrate how the approach can be implemented in a specific corporate setting and to show the methods used to test and validate the model, the authors illustrate a detailed application of the approach by using data from the airline industry. Their framework enables what-if evaluation of marketing return on investment, which can include such criteria as return on quality, return on advertising, return on loyalty programs, and even return on corporate citizenship, given a particular shift in customer perceptions. This enables the firm to focus marketing efforts on strategic initiatives that generate the greatest return.

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“…Degeratu [5] compares two estimation methods for choice models with last choice feedback,one method ignores the impact of unobserved heterogeneity,another method is approximate this impact by a stochastic relationship.Crawford and Shum [6] use finite mixtures to control for patient-level unobserved heterogeneity in estimating a dynamic matching model of pharmaceutical demand. Gowrisankaran,Mitchell and Moro [7] estimate a dynamic model of voter behavior with finite mixtures.Hiroyuki Kasahara and Katsumi Shimotsu [8] study nonparametric identifiability of type probabilities and typespecific component distributions in finite mixture models of dynamic discrete choices.…”

Section: Introductionmentioning

confidence: 99%

The Multinomial Logit Model with Last Choice Feedback

Huang

2009

2009 Second International Conference on Intelligent Computation Technology and Automation

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This paper proposes a method to develop the multinomial logit(MNL) model for a sequence of choices behavior that incorporates the probability of last choice in the current representative utility.The function of last probability could be additionally used as the coefficients of some part of observed component,while other alternative-specific coefficients are assumed to be variable only with their initial coefficients which are derived from the MNL model.Without loss of simplicity and readily interpretation,the model reflects the impact of last choice on observed component via last probability.The experiment based on synthetic data shows the fact that a person is aware of a alternative is willing to choose the alternative in following periods.The probability approaches a constant, and the bigger the proportion of observed component that influenced by last probability is,the higher the final probability become.

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Estimation bias in choice models with last choice feedback

Cited by 10 publications

References 32 publications

A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area

A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area

Return on Marketing: Using Customer Equity to Focus Marketing Strategy

The Multinomial Logit Model with Last Choice Feedback

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