nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2020‒07‒13
seven papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. The Economic Analysis of Consumer Attitudes Towards Food Produced Using Prohibited Production Methods: Do Consumers Really Care? By Kelvin Balcombe; Dylan Bradley; Iain Fraser
  2. Willingness to Pay for Better Air Quality: The case of China By Li-Qiu Liu; Zhong-Ling Yin; Bai-Chen Xie; Wei Zhou
  3. On the role of probability weighting on WTP for crop insurance with and without yield skewness By Douadia Bougherara; Laurent Piet
  4. Subjective Information Choice Processes By David Dillenberger; R. Vijay Krishna; Philipp Sadowski
  5. Semiparametric Estimation of Dynamic Binary Choice Panel Data Models By Fu Ouyang; Thomas Tao Yang
  6. On the observable restrictions of limited consideration models: theory and application By Yuta Inoue; Koji Shirai
  7. Likelihood inference on semiparametric models with generated regressors By Matsushita, Yukitoshi; Otsu, Taisuke

  1. By: Kelvin Balcombe; Dylan Bradley; Iain Fraser
    Abstract: We report the findings from a hypothetical discrete choice experiment (DCE) examining UK consumer attitudes for food produced using agricultural production methods currently prohibited in the UK i.e., hormone implants in beef; Ractopamine in pig feed; chlorine washed chicken; and Atrazine pesticide. Our results reveal that on average the public have very negative values for these forms of agricultural production methods. We also find that respondents highly value food products that observe EU food safety standards. Our willingness to pay estimates show that the positive values for food safety are frequently greater than negative values placed on the food production methods examined. Similarly, UK country of origin was highly valued but organic production was not valued as highly. These results clearly indicate that the only attribute that is negatively valued across all DCE are the production methods that are currently not allowed within the EU or UK.
    Keywords: Discrete Choice Experiment; Willingness to Pay
    Date: 2020–06
  2. By: Li-Qiu Liu (College of Management and Economics, Tianjin University); Zhong-Ling Yin (College of Management and Economics, Tianjin University); Bai-Chen Xie (College of Management and Economics, Tianjin University); Wei Zhou (EPRG, CJBS, Univrsity of Cambridge)
    Keywords: Happiness, Willingness to pay, Air pollution, China
    JEL: L94
    Date: 2020–05
  3. By: Douadia Bougherara (CEE-M - Centre d'Economie de l'Environnement - Montpellier - FRE2010 - INRA - Institut National de la Recherche Agronomique - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique - Montpellier SupAgro - Institut national d’études supérieures agronomiques de Montpellier); Laurent Piet (SMART - Structures et Marché Agricoles, Ressources et Territoires - AGROCAMPUS OUEST - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: A growing number of studies in finance and economics seek to explain insurance choices using the assumptions advanced by behavioral economics. One recent example in agricultural economics is the use of cumulative prospect theory (CPT) to explain farmer choices regarding crop insurance coverage levels (Babcock, 2015). We build upon this framework by deriving willingness to pay (WTP) for insurance programs under alternative assumptions, thus extending the model to incorporate farmer decisions regarding whether or not to purchase insurance. Our contribution is twofold. First, we study the sensitivity of farmer WTP for crop insurance to the inclusion of CPT parameters. We find that loss aversion and probability distortion increase WTP for insurance while risk aversion decreases it. Probability distortion in losses plays a particularly important role. Second, we study the impact of yield distribution skewness on farmer WTP assuming CPT preferences. We find that WTP decreases when the distribution of yields moves from negatively- to positively-skewed and that the combined effect of probability weighting in losses and skewness has a large negative impact on farmer WTP for crop insurance.
    Keywords: Crop Insurance,Cumulative Prospect Theory,premium subsidy,skewness
    Date: 2020–06–05
  4. By: David Dillenberger (University of Pennsylvania); R. Vijay Krishna (Florida State University); Philipp Sadowski (Duke University)
    Abstract: We propose a class of dynamic models that capture subjective (and hence unob-servable) constraints on the amount of information a decision maker can acquire, pay attention to, or absorb, via an Information Choice Process (icp). An icp specifies the information that can be acquired about the payo?-relevant state in the current period, and how this choice a?ects what can be learned in the future. In spite of their generality, wherein icps can accommodate any dependence of the information constraint on the history of information choices and state realizations, we show that the constraints imposed by them are identified up to a dynamic extension of Blackwell dominance. All the other parameters of the model are also uniquely identified. Behaviorally, the model is characterized by a novel recursive application of static properties.
    Keywords: Dynamic Preferences, Information Choice Process, Dynamic Blackwell Dominance, Rational Inattention, Subjective Markov Decision Process
    JEL: D80 D81 D90
    Date: 2020–03–06
  5. By: Fu Ouyang (School of Economics, University of Queensland); Thomas Tao Yang (Australian National University)
    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 Honor´e 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 Honor´e 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.
    Keywords: Bundle choices; rank estimation; panel data; bootstrap.
    JEL: C13 C14 C35
    Date: 2020–04–29
  6. By: Yuta Inoue (Faculty of Political Science and Economics, Waseda University); Koji Shirai (School of Economics, Kwansei Gakuin University)
    Abstract: This paper develops revealed preference analysis for limited consideration models. A revealed preference test is given for the decision model obeying two well-established hypotheses on a decision maker fs consideration: the attention filter property and competition filter property. We also provide a test for the two-step decision model called the (transitive) rational shortlist method. As an application, we conducted a simulation to compare the relative strength of observable restrictions across leading models, in addition to an experiment to compare models in terms of Selten fs index, which is a measure for plausibility of a model in explaining a given data set.
    Keywords: ; Revealed preference; Limited consideration; Limited attention; Rational short- listing; Bronars f test; Selten fs index
    JEL: C6 D1 D9
    Date: 2020–06
  7. By: Matsushita, Yukitoshi; Otsu, Taisuke
    Abstract: Hahn and Ridder (2013) formulated influence functions of semiparametric three step estimators where generated regressors are computed in the first step. This class of esti- mators covers several important examples for empirical analysis, such as production function estimators by Olley and Pakes (1996) and propensity score matching estimators for treatment effects by Heckman, Ichimura and Todd (1998). The present paper studies a nonparametric likelihood-based inference method for the parameters in such three step estimation problems. In particular, we apply the general empirical likelihood theory of Bravo, Escanciano and van Keilegom (2018) to modify semiparametric moment functions to account for influences from plug-in estimates into the above important setup, and show that the resulting likelihood ratio statistic becomes asymptotically pivotal without undersmoothing in the first and second step nonparametric estimates.
    JEL: J1
    Date: 2019–09–04

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