nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2017‒05‒07
seven papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Granger causality in dynamic binary short panel data models By Francesco Bartolucci; Claudia Pigini
  2. Can a Repeated Opt-Out Reminder remove hypothetical bias in discrete choice experiments? An application to consumer valuation of novel food products By Mohammed H. Alemu; Søren B. Olsen
  3. You can(’t) always get the job you want: stated versus revealed employment preferences in the Peruvian agro-industry By Schuster, Monica; Vranken, Liesbet; Maertens, Miet
  4. Discretizing unobserved heterogeneity By Stéphane Bonhomme; Thibaut Lamadon; Elena Manresa
  5. Choice in the presence of experts: the role of general practitioners in patients' hospital choice By Walter Beckert; Kate Collyer
  6. Semiparametric Estimation of the Random Utility Model with Rank-Ordered Choice Data By Jin Yan; Hong Il Yoo
  7. Revealed preferences over risk and uncertainty By Matthew Polisson; John K.-H. Quah; Ludovic Renou

  1. By: Francesco Bartolucci (Universita' di Perugia); Claudia Pigini (Universita' Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali)
    Abstract: Strict exogeneity of covariates other than the lagged dependent variable, and conditional on unobserved heterogeneity, is often required for consistent estimation of binary panel data models. This assumption is likely to be violated in practice because of feedback e ects from the past of the outcome variable on the present value of covariates and no general solution is yet available. In this paper, we provide the conditions for a logit model formulation that takes into account feedback e ects without specifying a joint parametric model for the outcome and predetermined explanatory variables. Our formulation is based on the equivalence between Granger's de nition of noncausality and a modi cation of the Sims' strict exogeneity assumption for nonlinear panel data models, introduced by Chamberlain (1982) and for which we provide a more general theorem. We further propose estimating the model parameters with a recent xed-e ects approach based on pseudo conditional inference, adapted to the present case, thereby taking care of the correlation between individual permanent unobserved heterogeneity and the model's covariates as well. Our results hold for short panels with a large number of cross-section units, a case of great interest in microeconomic applications.
    Keywords: binary panel data, fixed e ects, feedback e ects, pseudo-conditional inference
    JEL: C12 C23 C25
    Date: 2017–04
  2. By: Mohammed H. Alemu (Department of Food and Resource Economics, University of Copenhagen); Søren B. Olsen (Department of Food and Resource Economics, University of Copenhagen)
    Abstract: Recent papers have suggested that use of a so-called Repeated Opt-Out Reminder (ROOR) might mitigate hypothetical bias in stated Discrete Choice Experiments (DCE), but evidence so far has only been circumstantial. We provide the first comprehensive test of whether a ROOR can actually mitigate hypothetical bias in stated DCE. The data originates from a field experiment concerning consumer preferences for a novel food product made from cricket flour. Utilizing a between-subject design with three treatments, we find significantly higher marginal willingness to pay values in hypothetical than in nonhypothetical settings, confirming the usual presence of hypothetical bias. Comparing this to a hypothetical setting where the ROOR is introduced, we find that the ROOR effectively eliminates hypothetical bias for one attribute and significantly reduces it for the rest of the attributes. Our results further suggest that these reductions of hypothetical bias are brought about by a decrease in the tendency to ignore the price attribute.
    Keywords: Hypothetical bias, novel food, repeated opt-out reminder, willingness to pay
    JEL: C12 C13 C83 C93 D12 Q01 Q11 Q13 Q18
    Date: 2017–04
  3. By: Schuster, Monica; Vranken, Liesbet; Maertens, Miet
    Abstract: Employment in high-value agro-export sectors has been recognized to entail the potential to contribute to poverty reduction in rural areas of developing countries. Concerns have yet been raised about the quality of the created employment and worker preferences have often been overlooked in the literature. We use a discrete choice experiment, in which we relate stated and revealed employment preference of agro-industrial export workers in Peru. We explain employment (mis)matches as a function of personal and employer characteristics. Results suggest that employment preferences are heterogeneous, but that labor market frictions are smaller than what is commonly expected in developing country contexts.
    Keywords: employment conditions; stated and revealed preferences; discrete choice experiment; horticultural exports; Peru
    JEL: J24 J43 J81 O54 Q17
    Date: 2017–03
  4. By: Stéphane Bonhomme (Institute for Fiscal Studies and University of Chicago); Thibaut Lamadon (Institute for Fiscal Studies); Elena Manresa (Institute for Fiscal Studies and MIT)
    Abstract: We study panel data estimators based on a discretization of unobserved heterogeneity when individual heterogeneity is not necessarily discrete in the population. We focus on two-step grouped- fixed effects estimators, where individuals are classi ed into groups in a rst step using kmeans clustering, and the model is estimated in a second step allowing for group-speci c heterogeneity. We analyze the asymptotic properties of these discrete estimators as the number of groups grows with the sample size, and we show that bias reduction techniques can improve their performance. In addition to reducing the number of parameters, grouped fixed-effects methods provide e ective regularization. When allowing for the presence of time-varying unobserved heterogeneity, we show they enjoy fast rates of convergence depending of the underlying dimension of heterogeneity. Finally, we document the nite sample properties of two-step grouped fi xed-effects estimators in two applications: a structural dynamic discrete choice model of migration, and a model of wages with worker and rm heterogeneity.
    Keywords: Dimension reduction, panel data, structural models, kmeans clustering.
    Date: 2017–03–22
  5. By: Walter Beckert (Institute for Fiscal Studies and Birkbeck, University of London); Kate Collyer (Institute for Fiscal Studies)
    Abstract: This paper considers the micro-econometric analysis of patients' hospital choice for elective medical procedures when their choice set is pre-selected by a general practitioner (GP). It proposes a two-stage choice model that encompasses both, patient and GP level optimization, and it discusses identifi cation. The empirical analysis demonstrates biases and inconsistencies that arise when strategic pre-selection is not properly taken into account. We fi nd that patients defer to GPs when assessing hospital quality and focus on tangible attributes, like hospital amenities; and that GPs, in turn, as patients' agents present choice options based on quality, but as agents of health authorities also consider their financial implications.
    Keywords: Discrete choice, patient, principal, GP, agent, expert, endogenous choice sets, competition, hospital choice, elective medical procedure.
    Date: 2016–11–14
  6. By: Jin Yan (The Chinese University of Hong Kong.); Hong Il Yoo (Durham Business School)
    Abstract: We propose two semiparametric methods for estimating the random utility model using rank-ordered choice data. The framework is “semiparametric” in that the utility index includes finite dimensional preference parameters but the error term follows an unspecified distribution. Our methods allow for a flexible form of heteroskedasticity across individuals. With complete preference rankings, our methods also allow for heteroskedastic and correlated errors across alternatives, as well as a variety of random coefficients distributions. The baseline method we develop is the generalized maximum score (GMS) estimator, which is strongly consistent but follows a non-standard asymptotic distribution. To facilitate statistical inferences, we make extra regularity assumptions and develop the smoothed GMS estimator, which is asymptotically normal. Monte Carlo experiments show that our estimators perform favorably against popular parametric estimators under a variety of stochastic specifications
    Keywords: Rank-ordered; Random utility; Semiparametric estimation; Smoothing
    JEL: C14 C35
    Date: 2017–04
  7. By: Matthew Polisson (University of St Andrews); John K.-H. Quah (Johns Hopkins University); Ludovic Renou (Queen Mary University of London)
    Abstract: We develop a nonparametric procedure, called the lattice method, for testing the consistency of contingent consumption data with a broad class of models of choice under risk and under uncertainty. Our method allows for risk loving and elation seeking behavior and can be used to calculate, via Afriat's efficiency index, the magnitude of violations from a particular model of choice. We evaluate the performance of different models (including expected utility, disappointment aversion, rank dependent utility, mean-variance utility, and stochastically monotone utility) in the data collected by Choi et al. (2007), in terms of pass rates, power, and predictive success.
    Keywords: expected utility, rank dependent utility, disappointment aversion, Bronars power, predictive success, generalized axiom of revealed preference, first order stochastic dominance, mean-variance utility
    JEL: C14 C60 D11 D12 D81

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