nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2022‒03‒21
five papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models By Andriy Norets; Kenichi Shimizu
  2. Continuous permanent unobserved heterogeneity in dynamic discrete choice models By Jackson Bunting
  3. Semiparametric Estimation of Dynamic Binary Choice Panel Data Models By Fu Ouyang; Thomas Tao Yang
  4. Residential Neighbourhood Charging of Electric Vehicles: an exploration of user preferences By Budnitz, Hannah; Meelen, Toon; Schwanen, Tim
  5. Inferential Choice Theory By Narayanaswamy Balakrishnan; Efe A. Ok; Pietro Ortoleva

  1. By: Andriy Norets; Kenichi Shimizu
    Abstract: We propose a tractable semiparametric estimation method for dynamic discrete choice models. The distribution of additive utility shocks is modeled by location-scale mixtures of extreme value distributions with varying numbers of mixture components. Our approach exploits the analytical tractability of extreme value distributions and the flexibility of the location-scale mixtures. We implement the Bayesian approach to inference using Hamiltonian Monte Carlo and an approximately optimal reversible jump algorithm from Norets (2021). For binary dynamic choice model, our approach delivers estimation results that are consistent with the previous literature. We also apply the proposed method to multinomial choice models, for which previous literature does not provide tractable estimation methods in general settings without distributional assumptions on the utility shocks. We develop theoretical results on approximations by location-scale mixtures in an appropriate distance and posterior concentration of the set identified utility parameters and the distribution of shocks in the model.
    Date: 2022–02
  2. By: Jackson Bunting
    Abstract: In dynamic discrete choice (DDC) analysis, it is common to use finite mixture models to control for unobserved heterogeneity -- that is, by assuming there is a finite number of agent `types'. However, consistent estimation typically requires both a priori knowledge of the number of agent types and a high-level injectivity condition that is difficult to verify. This paper provides low-level conditions for identification of continuous permanent unobserved heterogeneity in dynamic discrete choice (DDC) models. The results apply to both finite- and infinite-horizon DDC models, do not require a full support assumption, nor a large panel, and place no parametric restriction on the distribution of unobserved heterogeneity. Furthermore, I present a seminonparametric estimator that is computationally attractive and can be implemented using familiar parametric methods. Finally, in an empirical application, I apply this estimator to the labor force participation model of Altug and Miller (1998). In this model, permanent unobserved heterogeneity may be interpreted as individual-specific labor productivity, and my results imply that the distribution of labor productivity can be estimated from the participation model.
    Date: 2022–02
  3. By: Fu Ouyang; Thomas Tao Yang
    Abstract: We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model we consider has the same random utility framework as in Honore and Kyriazidou (2000). We demonstrate that, with additional serial dependence conditions on the process of deterministic utility and tail restrictions on the error distribution, the (point) identification of the model can proceed in two steps, and only requires matching the value of an index function of explanatory variables over time, as opposed to that of each explanatory variable. Our identification approach motivates an easily implementable, two-step maximum score (2SMS) procedure -- producing estimators whose rates of convergence, in contrast to Honore and Kyriazidou's (2000) methods, are independent of the model dimension. We then derive the asymptotic properties of the 2SMS procedure and propose bootstrap-based distributional approximations for inference. Monte Carlo evidence indicates that our procedure performs adequately in finite samples. We then apply the proposed estimators to study labor market dependence and the effects of health shocks, using data from the Household, Income and Labor Dynamics in Australia (HILDA) survey.
    Date: 2022–02
  4. By: Budnitz, Hannah; Meelen, Toon; Schwanen, Tim
    Abstract: In this study, we investigate the preferences for private electric vehicle (EV) charging among households without a private residential charging option. We seek to understand which attributes of a residential neighbourhood charging service would offer an attractive substitute to charging an EV on private property overnight, as is most common among existing EV owners. Our stated choice experiment is designed to reflect preferences for parking as well as charging behaviour in order to ground the choices in trade-offs familiar to a target market representative of car drivers who are unlikely to be able to charge at home. Our findings suggest that this target population has different socio-demographic characteristics from the early adopters of EVs, and that therefore their priorities and preferences are different. Whether on-street or in a car park, the local environment in which the EV charging service sits and the experience of walking home after plugging in the vehicle is of primary importance. Some will also value the certainty of an available space over its convenience.
    Date: 2022–02–05
  5. By: Narayanaswamy Balakrishnan (McMaster University); Efe A. Ok (New York University); Pietro Ortoleva (Princeton University)
    Abstract: Despite being the fundamental primitive of the study of decision-making in economics, choice correspondences are not observable: even for a single menu of options, we observe at most one choice of an individual at a given point in time, as opposed to the set of all choices she deems most desirable in that menu. However, it may be possible to observe a person choose from a feasible menu at various times, repeatedly. We propose a method of inferring the choice correspondence of an individual from this sort of choice data. First, we derive our method axiomatically, assuming an ideal dataset. Next, we develop statistical techniques to implement this method for real-world situations where the sample at hand is often fairly small. As an application, we use the data of two famed choice experiments from the literature to infer the choice correspondences of the participating subjects.
    Keywords: Choice Correspondences, Estimation, Stochastic Choice Functions, Transitivity of Preferences
    JEL: C81 D11 D12 D81
    Date: 2021–02

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