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Journal articles on the topic 'Discrete choice'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Vega-Bayo, Ainhoa, and Petr Mariel. "A Discrete Choice Experiment Application to School Choice." Revista Hacienda Pública Española 230, no. 3 (2019): 41–62. http://dx.doi.org/10.7866/hpe-rpe.19.3.2.

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2

Kerbler, Boštjan. "Discrete choice models." Urbani izziv 17, no. 1-2 (2006): 134–38. http://dx.doi.org/10.5379/urbani-izziv-en-2006-17-01-02-017.

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3

Hu, Ling, and Peter C. B. Phillips. "Nonstationary discrete choice." Journal of Econometrics 120, no. 1 (2004): 103–38. http://dx.doi.org/10.1016/s0304-4076(03)00208-2.

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4

Bordley, Robert. "Discrete choice with large choice sets." Economics Letters 118, no. 1 (2013): 13–15. http://dx.doi.org/10.1016/j.econlet.2012.05.010.

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5

Antonio, Anna Liza M., Robert E. Weiss, Christopher S. Saigal, Ely Dahan, and Catherine M. Crespi. "A Bayesian hierarchical model for discrete choice data in health care." Statistical Methods in Medical Research 27, no. 12 (2017): 3544–59. http://dx.doi.org/10.1177/0962280217704226.

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In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identi

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6

Ramalho, E. A., and R. J. Smith. "Discrete Choice Non-Response." Review of Economic Studies 80, no. 1 (2012): 343–64. http://dx.doi.org/10.1093/restud/rds018.

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7

ZHANG, JUNSEN, and SAUL D. HOFFMAN. "Discrete-Choice Logit Models." Sociological Methods & Research 22, no. 2 (1993): 193–213. http://dx.doi.org/10.1177/0049124193022002002.

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8

Anderson, Simon P., and André de Palma. "Reverse discrete choice models." Regional Science and Urban Economics 29, no. 6 (1999): 745–64. http://dx.doi.org/10.1016/s0166-0462(99)00009-5.

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9

Smirnov, Oleg A. "Modeling spatial discrete choice." Regional Science and Urban Economics 40, no. 5 (2010): 292–98. http://dx.doi.org/10.1016/j.regsciurbeco.2009.09.004.

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10

Nijkamp, Peter. "Discrete spatial choice analysis." Regional Science and Urban Economics 17, no. 1 (1987): 1–2. http://dx.doi.org/10.1016/0166-0462(87)90065-2.

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11

Chambers, Christopher P., Yusufcan Masatlioglu, Paulo Natenzon, and Collin Raymond. "Weighted Linear Discrete Choice." American Economic Review 115, no. 4 (2025): 1226–57. https://doi.org/10.1257/aer.20220130.

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We introduce a new model of stochastic choice that assigns each choice option a utility, along with a salience parameter reflecting economic frictions. We characterize our model behaviorally and investigate its comparative statics properties. We show that the model generates intuitive closed-form solutions in equilibrium settings where firms can choose price, quality, and advertising. In addition, we show that the model allows for flexible substitution patterns and changes in market shares across choice sets. We demonstrate that our model can be easily identified and can outperform alternative

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12

Ben-Akiva, Moshe, and Bruno Boccara. "Discrete choice models with latent choice sets." International Journal of Research in Marketing 12, no. 1 (1995): 9–24. http://dx.doi.org/10.1016/0167-8116(95)00002-j.

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13

Ales, Laurence, and Christopher Sleet. "Optimal Taxation of Income‐Generating Choice." Econometrica 90, no. 5 (2022): 2397–436. http://dx.doi.org/10.3982/ecta18542.

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Discrete location, occupation, skill, and hours choices of workers underpin their incomes. This paper analyzes the optimal taxation of discrete income‐generating choice. It derives optimal tax equations and Pareto test inequalities for mixed logit choice environments that can accommodate discrete and unstructured choice sets, rich preference heterogeneity, and complex aggregate cross‐substitution patterns between choices. These equations explicitly connect optimal taxes to societal redistributive goals and private substitution behavior, with the latter encoded as a substitution matrix that des

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14

Timmermans, H., and P. van der Waerden. "Modelling Sequential Choice Processes: The Case of Two-Stop Trip Chaining." Environment and Planning A: Economy and Space 24, no. 10 (1992): 1483–90. http://dx.doi.org/10.1068/a241483.

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Traditional decompositional preferences and choice studies are focused on the prediction of single choices, such as choice of shopping centre or transport mode. Discrete choice experiments are used to derive choice models that predict the probability of choosing a choice alternative as a function of its attributes. In this paper these traditional models are extended by addressing the problem of sequential choice behaviour. It is demonstrated how discrete choice experiments and universal logit models may be used to predict a choice sequence. The approach is illustrated for the problem of trip c

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15

Tran, Hung, and Tien Mai. "Network-based representations and dynamic discrete choice models for multiple discrete choice analysis." Transportation Research Part B: Methodological 184 (June 2024): 102948. http://dx.doi.org/10.1016/j.trb.2024.102948.

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16

Bastin, Fabian, Yan Liu, Cinzia Cirillo, and Tien Mai. "Transferring Time-Series Discrete Choice to Link-Based Route Choice in Space: Estimating Vehicle Type Preference using Recursive Logit Model." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 49 (2018): 81–90. http://dx.doi.org/10.1177/0361198118796731.

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This paper considers a sequential discrete choice problem in a time domain, formulated and solved as a route choice problem in a space domain. Starting from a dynamic specification of time-series discrete choices, we show how it is transferrable to link-based route choices that can be formulated by a finite path choice multinomial logit model. This study establishes that modeling sequential choices over time and in space are equivalent as long as the utility of the choice sequence is additive over the decision steps, the link-specific attributes are deterministic, and the decision process is M

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17

Subhro Mitra. "Discrete Choice Model of Agricultural Shipper's Mode Choice." Transportation Journal 52, no. 1 (2013): 6. http://dx.doi.org/10.5325/transportationj.52.1.0006.

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18

Akinc, Deniz, Deborah J. Street, and Martina Vandebroek. "Varying choice set sizes in discrete choice experiments." Journal of Choice Modelling 52 (September 2024): 100493. http://dx.doi.org/10.1016/j.jocm.2024.100493.

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19

Athey, Susan, and Guido W. Imbens. "DISCRETE CHOICE MODELS WITH MULTIPLE UNOBSERVED CHOICE CHARACTERISTICS*." International Economic Review 48, no. 4 (2007): 1159–92. http://dx.doi.org/10.1111/j.1468-2354.2007.00458.x.

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20

Claassen, Roger, and Abebayehu Tegene. "Agricultural Land Use Choice: A Discrete Choice Approach." Agricultural and Resource Economics Review 28, no. 1 (1999): 26–36. http://dx.doi.org/10.1017/s1068280500000940.

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A discrete choice model and site-specific data are used to analyze land use choices between crop production and pasture in the Corn Belt. The results show that conversion probabilities depend on relative returns, land quality, and government policy. In general it is found that landowners are less inclined to remove land from crop production than to convert land to crop production.

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21

Feldman, Paul, and John Rehbeck. "Revealing a preference for mixtures: An experimental study of risk." Quantitative Economics 13, no. 2 (2022): 761–86. http://dx.doi.org/10.3982/qe1694.

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Using a revealed preference approach, we conduct an experiment where subjects make choices from linear convex budgets in the domain of risk. We find that many individuals prefer mixtures of lotteries in ways that systematically rule out expected utility behavior. We explore the extent to which an individual's preference to choose mixtures is related to a preference for randomization by comparing choices from a convex choice task to the decisions made in a repeated discrete choice task. We find that a preference to mix is positively correlated with behavior from repeated discrete choice tasks.

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22

Working, Amanda, Mohammed Alqawba, and Norou Diawara. "Dynamic Attribute-Level Best Worst Discrete Choice Experiments." International Journal of Marketing Studies 11, no. 2 (2019): 1. http://dx.doi.org/10.5539/ijms.v11n2p1.

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Dynamic modelling of decision maker choice behavior of best and worst in discrete choice experiments (DCEs) has numerous applications. Such models are proposed under utility function of decision maker and are used in many areas including social sciences, health economics, transportation research, and health systems research. After reviewing references on the study of such experiments, we present example in DCE with emphasis on time dependent best-worst choice and discrimination between choice attributes. Numerical examples of the dynamic DCEs are simulated, and the associated expected utilitie

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23

Islam, Farahnaz, James F. Thrasher, Feifei Xiao, Robert R. Moran, and James W. Hardin. "Data management and techniques for best–worst discrete choice experiments." Stata Journal: Promoting communications on statistics and Stata 23, no. 4 (2023): 1020–44. http://dx.doi.org/10.1177/1536867x231212437.

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In this article, we present software that is suitable for use with Stata’s choice modeling suite of commands, which begin with cm. Within the context of choice models, we focus on best–worst data. In such data, respondents are presented a set of choices and are required to select a best and a worst choice from among the alternatives. Optionally, respondents may indicate an opt-out choice, in which no best or worst choice exists in the choice set. Such data are simplified versions of experiments in which respondents rank all the choices. Once best–worst data are collected, there are specific ty

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24

Signorino, Curtis S. "Structure and Uncertainty in Discrete Choice Models." Political Analysis 11, no. 4 (2003): 316–44. http://dx.doi.org/10.1093/pan/mpg020.

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Social scientists are often confronted with theories in which one or more actors make choices over a discrete set of options. In this article, I generalize a broad class of statistical discrete choice models, with both well-known and new nonstrategic and strategic special cases. I demonstrate how to derive statistical models from theoretical discrete choice models and, in doing so, I address the statistical implications of three sources of uncertainty: agent error, private information about payoffs, and regressor error. For strategic and some nonstrategic choice models, the three types of unce

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25

Kalwani, Manohar U., Robert J. Meyer, and Donald G. Morrison. "Benchmarks for Discrete Choice Models." Journal of Marketing Research 31, no. 1 (1994): 65. http://dx.doi.org/10.2307/3151947.

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26

Gönsch, Jochen, Robert Klein, and Claudius Steinhardt. "Discrete Choice Modelling (Teil I)." WiSt - Wirtschaftswissenschaftliches Studium 37, no. 7 (2008): 356–62. http://dx.doi.org/10.15358/0340-1650-2008-7-356.

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27

Gönsch, Jochen, Robert Klein, and Claudius Steinhardt. "Discrete Choice Modelling (Teil II)." WiSt - Wirtschaftswissenschaftliches Studium 37, no. 8 (2008): 412–18. http://dx.doi.org/10.15358/0340-1650-2008-8-412.

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28

Ballman, Karla V. "Discrete Choice Methods With Simulations." Journal of the American Statistical Association 100, no. 469 (2005): 351–52. http://dx.doi.org/10.1198/jasa.2005.s6.

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29

Perkins, W. S., and J. Roundy. "Discrete Choice Surveys by Telephone." Journal of the Academy of Marketing Science 21, no. 1 (1993): 33–38. http://dx.doi.org/10.1177/0092070393211004.

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30

Kalwani, Manohar U., Robert J. Meyer, and Donald G. Morrison. "Benchmarks for Discrete Choice Models." Journal of Marketing Research 31, no. 1 (1994): 65–75. http://dx.doi.org/10.1177/002224379403100106.

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In assessing the performance of a choice model, we have to answer the question, “Compared with what?” Analyses of consumer brand choice data historically have measured fit by comparing a model's performance with that of a naive model that assumes a household's choice probability on each occasion equals the aggregate market share of each brand. The authors suggest that this benchmark could form an overly naive point of reference in assessing the fit of a choice model calibrated on scanner-panel data, or any repeated-measures analysis of choice. They propose that fairer benchmarks for discrete c

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31

Newey, Whitney K. "NONPARAMETRIC CONTINUOUS/DISCRETE CHOICE MODELS*." International Economic Review 48, no. 4 (2007): 1429–39. http://dx.doi.org/10.1111/j.1468-2354.2007.00469.x.

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32

Heiss, Florian. "Discrete Choice Methods with Simulation." Econometric Reviews 35, no. 4 (2016): 688–92. http://dx.doi.org/10.1080/07474938.2014.975634.

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33

Li, Baibing, and David A. Hensher. "Risky weighting in discrete choice." Transportation Research Part B: Methodological 102 (August 2017): 1–21. http://dx.doi.org/10.1016/j.trb.2017.04.014.

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34

Misra, Sanjog. "Generalized Reverse Discrete Choice Models." Quantitative Marketing and Economics 3, no. 2 (2005): 175–200. http://dx.doi.org/10.1007/s11129-005-0260-3.

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35

Zheng, Xu. "Testing for discrete choice models." Economics Letters 98, no. 2 (2008): 176–84. http://dx.doi.org/10.1016/j.econlet.2007.04.027.

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36

Kanaroglou, Pavlos S. "Sampling and Discrete Choice Analysis." Professional Geographer 46, no. 3 (1994): 359–68. http://dx.doi.org/10.1111/j.0033-0124.1994.00359.x.

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37

Brock, W. A., and S. N. Durlauf. "Discrete Choice with Social Interactions." Review of Economic Studies 68, no. 2 (2001): 235–60. http://dx.doi.org/10.1111/1467-937x.00168.

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38

Herger, Nils, and Steve McCorriston. "On discrete location choice models." Economics Letters 120, no. 2 (2013): 288–91. http://dx.doi.org/10.1016/j.econlet.2013.04.015.

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39

Bhatta, Bharat P. "Theoretical, methodological and practical issues of choice models." International Journal of Entrepreneurship and Economic Issues 1, no. 1 (2018): 21–37. http://dx.doi.org/10.32674/ijeei.v1i0.4.

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This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

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40

Horowitz, Joel L., and Jordan J. Louviere. "Testing Predicted Choices Against Observations in Probabilistic Discrete-Choice Models." Marketing Science 12, no. 3 (1993): 270–79. http://dx.doi.org/10.1287/mksc.12.3.270.

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41

Barseghyan, Levon, Francesca Molinari, and Matthew Thirkettle. "Discrete Choice under Risk with Limited Consideration." American Economic Review 111, no. 6 (2021): 1972–2006. http://dx.doi.org/10.1257/aer.20190253.

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This paper is concerned with learning decision-makers’ preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain sufficient conditions for the model’s semi-nonparametric point identification, including in cases where consideration depends on preferences and on some of the exogenous variables. Our method yields an estimator that is easy to compute and is applicable in markets with large choice sets. We illustrate its properties using a dataset

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42

Desmet, Pierre. "Effectiveness of measures assessing response to price information." Journal of Product & Brand Management 25, no. 7 (2016): 676–86. http://dx.doi.org/10.1108/jpbm-06-2015-0908.

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Purpose Questionnaire measures of consumers’ willingness to pay (WTP) and price sensitivity are biased, yet these declarative methods can aid managerial decision-making. Additional choices involve which question formats to use (open-ended or discrete choice) and how many questions (unique versus multiple). This paper aims to inform such choices for online data collection with an empirical evaluation of the size of the bias induced by four methods (price acceptability, price judgements, multiple discrete choices and single discrete choices) in a realistic choice context. Design/methodology/appr

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43

Verma, R., G. R. Plaschka, B. Hanlon, A. Livingston, and K. Kalcher. "Predicting customer choice in services using discrete choice analysis." IBM Systems Journal 47, no. 1 (2008): 179–91. http://dx.doi.org/10.1147/sj.471.0179.

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44

Waldman, Donald M. "Estimation in Discrete Choice Models with Choice-Based Samples." American Statistician 54, no. 4 (2000): 303. http://dx.doi.org/10.2307/2685782.

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45

Waldman, Donald M. "Estimation in Discrete Choice Models with Choice-Based Samples." American Statistician 54, no. 4 (2000): 303–6. http://dx.doi.org/10.1080/00031305.2000.10474563.

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46

Rigby, Dan, Michael Burton, and Jo Pluske. "Preference Stability and Choice Consistency in Discrete Choice Experiments." Environmental and Resource Economics 65, no. 2 (2015): 441–61. http://dx.doi.org/10.1007/s10640-015-9913-1.

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47

Mattmann, Matteo, Ivana Logar, and Roy Brouwer. "Choice certainty, consistency, and monotonicity in discrete choice experiments." Journal of Environmental Economics and Policy 8, no. 2 (2018): 109–27. http://dx.doi.org/10.1080/21606544.2018.1515118.

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48

Newman, Jeffrey, and Laurie Garrow. "Stacked Hybrid Discrete Choice Models for Airline Itinerary Choice." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 12 (2020): 243–53. http://dx.doi.org/10.1177/0361198120953149.

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This study develops a methodology to train and apply a hybrid stacked discrete choice model for airline itinerary choice. This stacked model framework includes a data-driven component (i.e., gradient boosting machines) as well as a theory-driven component (i.e., utility maximization using generalized extreme value models). The resulting ensemble model combines attractive features of each, including the ability to conform to complex nonlinear relationships among itinerary characteristics, as well as the ability to leverage an analyst’s understanding of travel behavior tendencies and the natural

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49

Horowitz, J. L. "Modeling the Choice of Choice Set in Discrete-Choice Random-Utility Models." Environment and Planning A: Economy and Space 23, no. 9 (1991): 1237–46. http://dx.doi.org/10.1068/a231237.

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50

Eluru, Naveen. "Evaluating alternate discrete choice frameworks for modeling ordinal discrete variables." Accident Analysis & Prevention 55 (June 2013): 1–11. http://dx.doi.org/10.1016/j.aap.2013.02.012.

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