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on Discrete Choice Models |
| By: | Qinxin Guo (Graduate School of Economics, Kobe University, JAPAN); Junyi Shen (Research Institute for Economics and Business Administration, Kobe University, JAPAN) |
| Abstract: | As an alternative method to discrete choice experiments, best-worst scaling provides additional information about consumers, slightly lessens the burden of mental process, and shows better quality. However, its advantages were ambiguous in previous literature, since each case of the best-worst scaling contained distinct information, and results from comparisons with discrete choice experiment varied with different data. In this study, we applied a goodness of fit statistic named count R square in evaluating the best-worst scaling profile case, the discrete choice experiment, and the best-worst scaling multi-profile case by using data from a survey of preference for mobile payment. The results suggest that the best-worst multi-profile case surpasses other methods. We also compared the mixed logit model and the latent class model using three non-nested tests. The results indicate that the mixed logit model is superior to the latent class model in all three tests. |
| Keywords: | Discrete Choice Experiment; Best-worst Scaling; Goodness of Fit; Latent Class Model; Mixed Logit Model |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:kob:dpaper:dp2019-14&r=all |
| By: | Levon Barseghyan; Maura Coughlin; Francesca Molinari; Joshua C. Teitelbaum |
| Abstract: | We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between agents' choice sets and their preferences. We first establish that the model is partially identified and characterize its sharp identification region. We also show how the model can be used to assess the welfare cost of limited choice sets. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with relatively low levels of risk aversion and heterogeneous choice sets. We also find that a mixed logit model, as well as some familiar models of choice set formation, are rejected in our data. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1907.02337&r=all |
| By: | Michael Lechner; Gabriel Okasa |
| Abstract: | In econometrics so-called ordered choice models are popular when interest is in the estimation of the probabilities of particular values of categorical outcome variables with an inherent ordering, conditional on covariates. In this paper we develop a new machine learning estimator based on the random forest algorithm for such models without imposing any distributional assumptions. The proposed Ordered Forest estimator provides a flexible estimation method of the conditional choice probabilities that can naturally deal with nonlinearities in the data, while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference thereof and thus providing the same output as classical econometric estimators based on ordered logit or probit models. An extensive simulation study examines the finite sample properties of the Ordered Forest and reveals its good predictive performance, particularly in settings with multicollinearity among the predictors and nonlinear functional forms. An empirical application further illustrates the estimation of the marginal effects and their standard errors and demonstrates the advantages of the flexible estimation compared to a parametric benchmark model. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1907.02436&r=all |
| By: | Dellaert, B.G.C.; Johnson, E.J.; Baker, T. |
| Abstract: | Health insurance decisions are a challenge for many consumers and influence welfare, health outcomes, and longevity. Two choice architecture tools are examined that can improve these decisions: informed ordering of options (from best to worst) and choice set partitioning. It is hypothesized that these tools can improve choices by changing: (1) decision focus: the options in a set on which consumers focus their attention, and (2) decision strategy: how consumers integrate the different attributes that make up the options. The first experiment focuses on the mediating role of the hypothesized decision processes on consumer decision outcomes. The outcome results are validated further in a field study of over 40,000 consumers making actual health insurance choices and in two additional experiments. The results show that informed ordering and partitioning can reduce consumers’ mistakes by hundreds of dollars per year. They suggest that wise choice architecture interventions depend upon two factors: The quality of the user model possessed by the firm to predict consumers’ best choice and possible interactions among the ensemble of choice architecture tools. |
| Keywords: | choice architecture, decision-making, consumer decision process, health insurance choice, consumer welfare |
| Date: | 2019–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureri:117879&r=all |