nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2020‒05‒11
nine papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients By Masayuki Sawada; Kohei Kawaguchi
  2. Multinomial logit processes and preference discovery: outside and inside the black box By Simone Cerreia-Vioglio; Fabio Maccheroni; Massimo Marinacci
  3. How McFadden met Rockafellar and learnt to do more with less By Jesper R.-V. Soerensen; Mogens Fosgerau
  4. COVID-19 Information and Demand for Protective Gear in the UK By Sonia Oreffice; Climent Quintana-Domeque
  5. Understanding California Wildfire Evacuee Behavior and Joint Choice-Making By Wong, Stephen D.; Broader, Jacquelyn C; Walker, Joan L. PhD; Shaheen, Susan A. PhD
  6. Social distancing and contagion in a discrete choice model of COVID-19 By Baskozos, Giorgos; Galanis, Giorgos; Di Guilmi, Corrado
  7. A Multialternative Neural Decision Process By Simone Cerreia-Vioglio; Fabio Maccheroni; Massimo Marinacci
  8. Dynamic Reserves in Matching Markets By Orhan Ayg\"un; Bertan Turhan
  9. Computing Bayes: Bayesian Computation from 1763 to the 21st Century By Gael M. Martin; David T. Frazier; Christian P. Robert

  1. By: Masayuki Sawada; Kohei Kawaguchi
    Abstract: We propose an estimation procedure for discrete choice models of differentiated products with possibly high-dimensional product attributes. In our model, high-dimensional attributes can be determinants of both mean and variance of the indirect utility of a product. The key restriction in our model is that the high-dimensional attributes affect the variance of indirect utilities only through finitely many indices. In a framework of the random-coefficients logit model, we show a bound on the error rate of a $l_1$-regularized minimum distance estimator and prove the asymptotic linearity of the de-biased estimator.
    Date: 2020–04
  2. By: Simone Cerreia-Vioglio; Fabio Maccheroni; Massimo Marinacci
    Abstract: We provide two characterizations, one axiomatic and the other neuro-computational, of the dependence of choice probabilities on deadlines, within the widely used softmax representation, where pt (a; A) is the probability that alternative a is selected from the set A of feasible alternatives if t is the time available to decide, is a time dependent noise parameter measuring the unit cost of information, u is a time independent utility function, and is an alternative-specific bias that determines the initial choice probabilities and possibly reáects prior information. Our axiomatic analysis provides a behavioral foundation of softmax (also known as Multinomial Logit Model when is constant). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behavior. Jointly, the two approaches provide a thorough understanding of soft-maximization in terms of internal causes (neurophysiological mechanisms) and external e§ects (testable implications). Keywords: Discrete Choice Analysis, Drift Di§usion Model, Heteroscedastic Extreme Value Models, Luce Model, Metropolis Algorithm, Multinomial Logit Model, Quantal Response Equilib ium, Rational Inattention
    Date: 2020
  3. By: Jesper R.-V. Soerensen (Department of Economics, University of Copenhagen, Denmark); Mogens Fosgerau (Department of Economics, University of Copenhagen, Denmark)
    Abstract: We study the additive random utility model of discrete choice under minimal assumptions. We make no assumptions regarding the distribution of random utility components or the functional form of systematic utility components. Exploiting the power of convex analysis, we are nevertheless able to generalize a range of important results. We characterize demand with a generalized Williams-Daly-Zachary theorem. A similarly generalized version of Hotz-Miller inversion yields constructive partial identification of systematic utilities. Estimators based on our partial identification result remain well defined in the presence of zeros in demand. We also provide necessary and sufficient conditions for point identification.
    Keywords: Additive random utility model; Discrete choice; Convex duality; Demand inversion; Partial identification
    JEL: C25 C6 D11
    Date: 2020–12–17
  4. By: Sonia Oreffice (University of Surrey); Climent Quintana-Domeque (University of Exeter)
    Abstract: Amid the COVID-19 crisis in the UK, we study the demand and willingness to pay for hand sanitiser gel, disposable face masks and disposable gloves, and the role of information on tested people and COVID-19 deaths in explaining the demand and willingness to pay (WTP) for these products. The specific hypotheses to test and concrete questions to study were pre-registered in AsPredicted (#38962) on 10 April 2020, and an online survey was launched in Prolific on a sample of the UK general population representative by age, sex and ethnicity on 11 April 2020. We find that there is a demand for these products, estimate the average WTP for them, and show that the provision of information affected the demand (and WTP) for disposable face masks. Giving information on the numbers of COVID-19 cumulative tested people and COVID-19 cumulative deaths increases the stated demand for disposable face masks by about 8 percentage points [95% CI: 0.8, 15.1] and 11 percentage points [95% CI: 3.7, 18.2], respectively. We also investigate whether the provision of information affects donations to UK charities focusing on groups more vulnerable to the COVID-19 pandemic (Age UK, British Lung Foundation, Samaritans, and Women’s Aid), but find no evidence of any relevant effect. We do not find differences by sex in the average WTP, or in the effects of information on demands and donations.
    Keywords: coronavirus, demand, donations, hand sanitiser, hand sanitizer, face masks, gloves
    JEL: C99 D12 I12 I18
    Date: 2020–04
  5. By: Wong, Stephen D.; Broader, Jacquelyn C; Walker, Joan L. PhD; Shaheen, Susan A. PhD
    Abstract: For evacuations, people must make the critical decision to evacuate or stay followed by a multi-dimensional choice composed of concurrent decisions of their departure time, transportation mode, route, destination, and shelter type. These choices have important impacts on transportation response and evacuation outcomes. While extensive research has been conducted on hurricane evacuation behavior, little is known about wildfire evacuation behavior. To address this critical research gap, particularly related to joint choice-making in wildfires, we surveyed individuals impacted by the 2017 December Southern California Wildfires (n=226) and the 2018 Carr Wildfire (n=284). Using these data, we contribute to the literature in two key ways. First, we develop two simple binary choice models to evaluate and compare the factors that influence the decision to evacuate or stay. Mandatory evacuation orders and higher risk perceptions both increased evacuation likelihood. Individuals with children and with higher education were more likely to evacuate, while individuals with pets, homeowners, low-income households, long-term residents, and prior evacuees were less likely to evacuate. Second, we develop two portfolio choice models (PCMs), which jointly model choice dimensions to assess multi-dimensional evacuation choice. We find several similarities between wildfires including a joint preference for within-county and nighttime evacuations and a joint dislike for within-county and highway evacuations. To help build a transportation toolkit for wildfires, we provide a series of evidence-based recommendations for local, regional, and state agencies. For example, agencies should focus congestion reducing responses at the neighborhood level within or close to the mandatory evacuation zone.
    Keywords: Engineering, Evacuations, evacuee behavior, California wildfires, portfolio choice model, joint choice modeling
    Date: 2020–05–01
  6. By: Baskozos, Giorgos (University of Oxford); Galanis, Giorgos (Goldsmiths, University of London, Centre for Applied Macroeconomic Analysis, Australian National University ; and CRETA, University of Warwick); Di Guilmi, Corrado (University of Technology Sydney, Australia; and Centre for Applied Macroeconomic Analysis, Australian National University)
    Abstract: We present an epidemic model in which heterogenous agents choose whether to enact social distancing practices. The policy maker decides on the timing and the extent of policies that incentivise social distancing. We evaluate the consequences of interventions and find that: (i) the timing of intervention is paramount in slowing the contagion, and (ii) a delay cannot be compensated by stronger measures.
    Date: 2020
  7. By: Simone Cerreia-Vioglio; Fabio Maccheroni; Massimo Marinacci
    Abstract: We introduce an algorithmic decision process for multialternative choice that combines binary comparisons and Markovian exploration. We show that a functional property, transitivity, makes it testable.
    Date: 2020–05
  8. By: Orhan Ayg\"un; Bertan Turhan
    Abstract: We study a school choice problem under affirmative action policies where authorities reserve a certain fraction of the slots at each school for specific student groups, and where students have preferences not only over the schools they are matched to but also the type of slots they receive. Such reservation policies might cause waste in instances of low demand from some student groups. To propose a solution to this issue, we construct a family of choice functions, dynamic reserves choice functions, for schools that respect within-group fairness and allow the transfer of otherwise vacant slots from low-demand groups to high-demand groups. We propose the cumulative offer mechanism (COM) as an allocation rule where each school uses a dynamic reserves choice function and show that it is stable with respect to schools' choice functions, is strategy-proof, and respects improvements. Furthermore, we show that transferring more of the otherwise vacant slots leads to strategy-proof Pareto improvement under the COM.
    Date: 2020–05
  9. By: Gael M. Martin; David T. Frazier; Christian P. Robert
    Abstract: The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characterize Bayesian inference, with the rules of probability used to transform prior probability distributions for all unknowns - models, parameters, latent variables - into posterior distributions, subsequent to the observation of data. Conducting Bayesian inference requires the evaluation of integrals in which these probability distributions appear. Bayesian computation is all about evaluating such integrals in the typical case where no analytical solution exists. This paper takes the reader on a chronological tour of Bayesian computation over the past two and a half centuries. Beginning with the one-dimensional integral first confronted by Bayes in 1763, through to recent problems in which the unknowns number in the millions, we place all computational problems into a common framework, and describe all computational methods using a common notation. The aim is to help new researchers in particular - and more generally those interested in adopting a Bayesian approach to empirical work - make sense of the plethora of computational techniques that are now on offer; understand when and why different methods are useful; and see the links that do exist, between them all.
    Keywords: history of Bayesian computation, Laplace approximation, Markov chain Monte Carlo, importance sampling, approximate Bayesian computation, Bayesian synthetic likelihood, variational Bayes, integrated nested Laplace approximation.
    JEL: C11 C15 C52
    Date: 2020

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