nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2022‒05‒30
eight papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Preferences for dynamic electricity tariffs: A comparison of households in Germany and Japan By Miwa Nakai; Victor von Loessl; Heike Wetzel
  2. Estimation of Recursive Route Choice Models with Incomplete Trip Observations By Tien Mai; The Viet Bui; Quoc Phong Nguyen; Tho V. Le
  3. Climate Change and Individual Behavior By Bernard, René; Tzamourani, Panagiota; Weber, Michael
  4. Micromobility Trip Characteristics, Transit Connections, and COVID-19 Effects By Fukushige, Tatsuya MS; Fitch, Dillon T. PhD; Mohiuddin, Hossain MS; Andersen, Hayden BS; Jenn, Alan PhD
  5. Optimal Discrete Decisions when Payoffs are Partially Identified By Timothy Christensen; Hyungsik Roger Moon; Frank Schorfheide
  6. Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets By Masanori Hirano; Hiroki Sakaji; Kiyoshi Izumi
  7. The Eurovision Song Contest: Voting Rules, Biases and Rationality By Victor Ginsburgh; Juan D. Moreno-Ternero
  8. A Deterministic Approximation Approach to the Continuum Logit Dynamic with an Application to Supermodular Games By Ratul Lahkar; Sayan Mukherjee; Souvik Roy

  1. By: Miwa Nakai (Fukui Prefectural University); Victor von Loessl (University of Kassel); Heike Wetzel (University of Kassel)
    Abstract: We evaluate a stated choice experiment on dynamic electricity tariffs based on two representative household surveys from Germany and Japan. Our results indicate significant differences between German and Japanese respondents’ preferences towards dynamic tariffs, with the latter generally being more open to dynamic pricing. Furthermore, our unique experimental design allows to disentangle preferences for inter- and intraday price changes, which are two essential tariff characteristics. In this respect, our results suggest that households need significant compensation in order to accept frequently changing price patters. In contrast, they are mostly indifferent with respect to the number of price changes per day. Besides the implementation of an environmental treatment message, we additionally investigate tariff characteristics, which aim at overcoming household acceptance barriers. To this end, a restrictive use of households’ consumption data, price caps, as well as highlighting the environmental benefits associated to dynamic tariffs present themselves as suitable tools to reduce households’ aversions against dynamic electricity tariffs.
    Keywords: Dynamic electricity tariffs, Stated choice experiment, Household acceptance barriers, Tariff design
    JEL: C35 D12 Q41
    Date: 2022
  2. By: Tien Mai; The Viet Bui; Quoc Phong Nguyen; Tho V. Le
    Abstract: This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue would be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation-maximization (EM) method that allows to deal with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method would be expensive, we propose a new estimation method based on the idea that the choice probabilities of unconnected link observations can be exactly computed by solving systems of linear equations. We further design a new algorithm, called as decomposition-composition (DC), that helps reduce the number of systems of linear equations to be solved and speed up the estimation. We compare our proposed algorithms with some standard baselines using a dataset from a real network and show that the DC algorithm outperforms the other approaches in recovering missing information in the observations. Our methods work with most of the recursive route choice models proposed in the literature, including the recursive logit, nested recursive logit, or discounted recursive models.
    Date: 2022–04
  3. By: Bernard, René; Tzamourani, Panagiota; Weber, Michael
    Abstract: Climate change poses large economic costs to governments and societies. Reducing individuals’ CO2 footprints is central in mitigating climate change. In a new paper, we show that providing information on combating climate change motivates individuals to take costly actions to offset CO2 emissions. Presenting the information as the result of scientific research is as effective as framing it as the behaviour of other people. Individuals' responses vary depending on their socio-demographic characteristics and attitudes towards climate change. Furthermore, individuals choose information that aligns with their views. Individuals who actively gather information about climate change have a higher willingness to pay for carbon offsets.
    Keywords: Climate change,information treatment,willingness to pay,information acquisition,CO2 compensation
    JEL: D10 D83 D91 Q54
    Date: 2022
  4. By: Fukushige, Tatsuya MS; Fitch, Dillon T. PhD; Mohiuddin, Hossain MS; Andersen, Hayden BS; Jenn, Alan PhD
    Abstract: While micromobility services (e.g., bikeshare, e-bike share, e-scooter share) hold great potential for providing clean travel, estimating the effects of those services on vehicle miles traveled and reducing greenhouse gases is challenging. To address some of the challenges, this study examined survey, micromobility, and transit data collected from 2017 to 2021 in approximately 20 U.S. cities. Micromobility fleet utilization ranged widely from 0.7 to 12 trips per vehicle per day, and the average trip distance was 0.8 to 3.6 miles. The median (range) rates at which micromobility trips substituted for other modes were 41% (16–71%) for car trips, 36% (5–48%) for walking, and 8% (2–35%) for transit, 5% (2–42%) for no trip. In most cities, the mean actual trip distance was approximately 1.5 to 2 times longer than the mean distance of a line connecting origin to destination. There was a weak and unclear connection between micromobility use and transit use that requires further study to more clearly delineate, but micromobility use had a stronger positive relationship to nearby rail use than to nearby bus use in cities with rail and bus service. The COVID-19 pandemic led to more moderate declines in docked than in dockless bike-share systems. Metrics that would enable better assessment of the impacts of micromobility are vehicle miles traveled and emissions of micromobility fleets and their service vehicles, and miles and percentage of micromobility trips that connect to transit or substitute for car trips.
    Keywords: Engineering, Micromobility, sustainable transportation, public transit, travel behavior, mode choice, performance metrics, COVID-19
    Date: 2022–05–01
  5. By: Timothy Christensen; Hyungsik Roger Moon; Frank Schorfheide
    Abstract: We derive optimal statistical decision rules for discrete choice problems when the decision maker is unable to discriminate among a set of payoff distributions. In this problem, the decision maker must confront both model uncertainty (about the identity of the true payoff distribution) and statistical uncertainty (the set of payoff distributions must be estimated). We derive "efficient-robust decision rules" which minimize maximum risk or regret over the set of payoff distributions and which use the data to learn efficiently about features of the set of payoff distributions germane to the choice problem. We discuss implementation of these decision rules via the bootstrap and Bayesian methods, for both parametric and semiparametric models. Using a limits of experiments framework, we show that efficient-robust decision rules are optimal and can dominate seemingly natural alternatives. We present applications to treatment assignment using observational data and optimal pricing in environments with rich unobserved heterogeneity.
    Date: 2022–04
  6. By: Masanori Hirano; Hiroki Sakaji; Kiyoshi Izumi
    Abstract: This study proposes a new generative adversarial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fake orders in continuous spaces because of GAN architectures' learning limitations. However, in reality, the orders are discrete, such as order prices, which has minimum order price unit, or order types. Thus, we change the generation method to place the generated fake orders into discrete spaces in this study. Because this change disabled the ordinary GAN learning algorithm, this study employed a policy gradient, frequently used in reinforcement learning, for the learning algorithm. Through our experiments, we show that our proposed model outperforms previous models in generated order distribution. As an additional benefit of introducing the policy gradient, the entropy of the generated policy can be used to check GAN's learning status. In the future, higher performance GANs, better evaluation methods, or the applications of our GANs can be addressed.
    Date: 2022–04
  7. By: Victor Ginsburgh; Juan D. Moreno-Ternero
    Abstract: We analyze and evaluate the rules and results at the 2021 Eurovision Song Contest. We first concentrate on the various voting procedures, and explore several alternatives (inspired by classical contributions in social choice and game theory) that could make a difference for the results. We also discuss other important issues, such as simplicity, contrast effects and whether experts are better judges than tele-voters. Our findings raise the question of whether the voting procedures used by the Eurovision Song Contest authorities are fail-safe. We endorse instead the use of the so-called Shapley voting procedure for judges as well as tele-voters.
    Keywords: Eurovision Song Contest, Voting, Borda, Shapley Method, Biases
    Date: 2022–05
  8. By: Ratul Lahkar (Ashoka University); Sayan Mukherjee (ISI Kolkata); Souvik Roy (ISI, Kolkata)
    Abstract: We consider the logit dynamic in a large population game with a continuum of strategies. The deterministic approximation approach requires us to derive this dynamic as the finite horizon limit of a stochastic process in a game with a finite but large number of strategies and players. We first establish the closeness of this dynamic with a step–wise approximation. We then show that the logit stochastic process is close to the step–wise logit dynamic in a discrete approximation of the original game. Combining the two results, we obtain our deterministic approximation result. We apply the result to large population supermodular games with a continuum of strategies. Over finite but sufficiently long time horizons, the logit stochastic process converges to logit equilibria in a discrete approximation of the supermodular game. By the deterministic approximation approach, so does the logit dynamic in the continuum supermodular game
    Date: 2022–04–26

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