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Journal articles on the topic 'Discrete choice'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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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 (September 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 (May 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 (January 2013): 13–15. http://dx.doi.org/10.1016/j.econlet.2012.05.010.

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5

Ramalho, E. A., and R. J. Smith. "Discrete Choice Non-Response." Review of Economic Studies 80, no. 1 (April 6, 2012): 343–64. http://dx.doi.org/10.1093/restud/rds018.

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6

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

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7

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

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8

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

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9

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

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10

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

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11

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 describes cross‐sensitivities of choice distributions to tax‐induced utility variation. In repeated mixed logit settings, the substitution matrix is exactly the Markov matrix of shock‐induced agent transitions across choices. We describe implications of this equivalence for evaluation of prevailing tax designs and the structural estimation of optimal policy mixed logit models. We apply our results to two salient examples: spatial taxation and taxation of couples.

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12

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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13

Athey, Susan, and Guido W. Imbens. "DISCRETE CHOICE MODELS WITH MULTIPLE UNOBSERVED CHOICE CHARACTERISTICS*." International Economic Review 48, no. 4 (December 11, 2007): 1159–92. http://dx.doi.org/10.1111/j.1468-2354.2007.00458.x.

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14

Claassen, Roger, and Abebayehu Tegene. "Agricultural Land Use Choice: A Discrete Choice Approach." Agricultural and Resource Economics Review 28, no. 1 (April 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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15

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 (April 18, 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 identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.

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16

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

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17

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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18

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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19

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

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20

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

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21

Kalwani, Manohar U., Robert J. Meyer, and Donald G. Morrison. "Benchmarks for Discrete Choice Models." Journal of Marketing Research 31, no. 1 (February 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 choice models in marketing should incorporate heterogeneity in consumer choice probabilities, evidence for which is by now well documented in the marketing literature. They use simulated data to compare the performance of parametric and nonparametric benchmark models, which allow for heterogeneity in consumer choice probabilities, with the performance of the aggregate share-based benchmark model, which assumes consumers are homogeneous in their choice probabilities. They also assess the performance of two previously published consumer behavior models against the proposed fairer benchmark models that allow for heterogeneity in consumer choice probabilities. They find that one provides a significantly better fit than their more conservative benchmark models and the other performs less favorably.

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22

Newey, Whitney K. "NONPARAMETRIC CONTINUOUS/DISCRETE CHOICE MODELS*." International Economic Review 48, no. 4 (December 11, 2007): 1429–39. http://dx.doi.org/10.1111/j.1468-2354.2007.00469.x.

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23

Heiss, Florian. "Discrete Choice Methods with Simulation." Econometric Reviews 35, no. 4 (February 6, 2016): 688–92. http://dx.doi.org/10.1080/07474938.2014.975634.

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24

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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25

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

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26

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

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27

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

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28

Brock, W. A., and S. N. Durlauf. "Discrete Choice with Social Interactions." Review of Economic Studies 68, no. 2 (April 1, 2001): 235–60. http://dx.doi.org/10.1111/1467-937x.00168.

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29

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

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30

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 uncertainty produce different statistical models. In these cases, misspecifying the type of uncertainty leads to biased and inconsistent estimates, and to incorrect inferences based on estimated probabilities.

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31

Working, Amanda, Mohammed Alqawba, and Norou Diawara. "Dynamic Attribute-Level Best Worst Discrete Choice Experiments." International Journal of Marketing Studies 11, no. 2 (May 23, 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 utilities over time of the choice models are derived using Markov decision processes. The estimates are computationally consistent with decision choices over time.

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32

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 (October 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 chaining. The research findings support the suggested approach.

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33

Barseghyan, Levon, Francesca Molinari, and Matthew Thirkettle. "Discrete Choice under Risk with Limited Consideration." American Economic Review 111, no. 6 (June 1, 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 on property insurance purchases. (JEL D81, D83, D91, G22, G52)

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34

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

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35

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 (September 11, 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 Markovian. We employ the recursive logit model proposed in the context of route choice in a network, and apply it to estimate time-series vehicle type choice based on Maryland Vehicle Stated Preference Survey data. The model has been efficiently estimated by a dynamic programming approach; the values of estimated coefficients provide important patterns on vehicle type preferences. Compared with a naive model based on sequential multinomial logit choices which are independent over time and a dynamic discrete choice model which considers agent’s future expectations, the smaller root mean square error of recursive logit model indicates that it has a better performance in estimating sequential choices over time. We also compare the predictive powers and find that the proposed model outperforms the basic approach and the dynamic approach.

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36

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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37

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

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38

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

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39

Rigby, Dan, Michael Burton, and Jo Pluske. "Preference Stability and Choice Consistency in Discrete Choice Experiments." Environmental and Resource Economics 65, no. 2 (May 22, 2015): 441–61. http://dx.doi.org/10.1007/s10640-015-9913-1.

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40

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 (September 5, 2018): 109–27. http://dx.doi.org/10.1080/21606544.2018.1515118.

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41

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 (October 3, 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 relationship among itineraries. Using a real industry dataset containing purchase information for approximately 10 million air travelers, it is demonstrated that the resulting model outperforms either the gradient boosting or utility maximization modeling paradigm alone in forecasting air traveler choice behavior. Implementation of this model can be achieved using efficient open source tools including XGBoost and Larch, and requires relatively modest additional effort by an analyst above and beyond the effort to use either tool alone.

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42

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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43

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 (September 1991): 1237–46. http://dx.doi.org/10.1068/a231237.

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44

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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45

Bhatta, Bharat P. "Theoretical, methodological and practical issues of choice models." International Journal of Entrepreneurship and Economic Issues 1, no. 1 (February 26, 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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46

Coast, Joanna, Terry N. Flynn, Chris Salisbury, Jordan Louviere, and Tim J. Peters. "Maximising Responses to Discrete Choice Experiments." Applied Health Economics and Health Policy 5, no. 4 (2006): 249–60. http://dx.doi.org/10.2165/00148365-200605040-00006.

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47

Ortelli, Nicola, Tim Hillel, Francisco C. Pereira, Matthieu de Lapparent, and Michel Bierlaire. "Assisted specification of discrete choice models." Journal of Choice Modelling 39 (June 2021): 100285. http://dx.doi.org/10.1016/j.jocm.2021.100285.

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48

Šimeček, Michal. "Discrete Choice Analysis of Travel Behaviour." Transactions on Transport Sciences 10, no. 1 (July 24, 2019): 5–9. http://dx.doi.org/10.5507/tots.2019.001.

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49

Carter, Robert E. "Incorporating Demographics into Discrete Choice Analyses." International Journal of Market Research 52, no. 3 (May 2010): 393–406. http://dx.doi.org/10.2501/s1470785310201338.

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50

Leake, Charles, Simon P. Anderson, Andre de Palma, and Jacques-Francois Thisse. "Discrete Choice Theory of Product Differentiation." Journal of the Operational Research Society 46, no. 4 (April 1995): 543. http://dx.doi.org/10.2307/2584603.

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