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on Discrete Choice Models |
| By: | Didier Nibbering |
| Abstract: | The number of parameters in a standard multinomial logit model increases linearly with the number of choice alternatives and number of explanatory variables. Since many modern applications involve large choice sets with categorical explanatory variables, which enter the model as large sets of binary dummies, the number of parameters in a multinomial logit model is often large. This paper proposes a new method for data-driven two-way parameter clustering over outcome categories and explanatory dummy categories in a multinomial logit model. A Bayesian Dirichlet process mixture model encourages parameters to cluster over the categories, which reduces the number of unique model parameters and provides interpretable clusters of categories. In an empirical application, we estimate the holiday preferences of 11 household types over 49 holiday destinations, and identify a small number of household segments with different preferences across clusters of holiday destinations. |
| Keywords: | large choice sets, Dirichlet process prior, multinomial logit model, highdimensional models |
| JEL: | C11 C14 C25 C35 C51 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2023-19&r=dcm |
| By: | Amrei Stammann |
| Abstract: | Naive maximum likelihood estimation of binary logit models with fixed effects leads to unreliable inference due to the incidental parameter problem. We study the case of three-dimensional panel data, where the model includes three sets of additive and overlapping unobserved effects. This encompasses models for network panel data, where senders and receivers maintain bilateral relationships over time, and fixed effects account for unobserved heterogeneity at the sender-time, receiver-time, and sender-receiver levels. In an asymptotic framework, where all three panel dimensions grow large at constant relative rates, we characterize the leading bias of the naive estimator. The inference problem we identify is particularly severe, as it is not possible to balance the order of the bias and the standard deviation. As a consequence, the naive estimator has a degenerating asymptotic distribution, which exacerbates the inference problem relative to other fixed effects estimators studied in the literature. To resolve the inference problem, we derive explicit expressions to debias the fixed effects estimator. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.04073&r=dcm |
| By: | Giulia Buccione (Brown University); Martín Rossi (Department of Economics, Universidad de San Andrés) |
| Abstract: | Adoption rates of safe drinking water are low in developing countries. In regions where centralized water treatment infrastructure is absent, the conventional policy response is to enhance access to safe water via point-of-use chlorination. Previous research, however, reports a ceiling in adoption rates of chlorinated water at 50 percent, even when chlorine is provided for free. We report experimental evidence that a cultural-friendly technology, which provides filtered water that resembles local ancestral water, leads to higher adoption rates and willingness to pay than usual chlorinated water provision. We document adoption rates of 91 percent for filtered water, 42 percentage points higher than for chlorinated water. Willingness to pay is 61 percent higher for filtered water compared to chlorinated water. Our findings suggest policymakers should redirect their efforts away from the current mainstream approach of subsidized chlorine and instead explore alternative strategies that consider local communities’ culture and preferences. |
| Keywords: | Middle East, water-borne diseases, field experiments |
| JEL: | D10 I10 C93 Q53 Z10 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:sad:wpaper:167&r=dcm |
| By: | Claudia Cerrone; Anujit Chakraborty; Hyok Jung Kim; Leonhard Lades (Department of Economics, University of California Davis) |
| Abstract: | We use a real-effort experiment to jointly estimate present bias (β) and sophistication (̂ β) parameters, separately over money (βm, ̂ βm) and effort (βe, ̂ βe). Our novel incentive structure aligns the choice scenario with the canonical assumption of choices being the interior optima of a concave utility-maximization exercise. Participants choose to (and predict to) complete 14% (and 10%) fewer tasks on the same day than on a future day, leading to an estimated βe between 0.70 and .79 (and ̂ βe between 0.80 and .88). We find no evidence of present bias or sophistication over money |
| Keywords: | present bias, sophistication, beliefs, experiment, real effort task, experiment |
| Date: | 2023–11–06 |
| URL: | http://d.repec.org/n?u=RePEc:cda:wpaper:359&r=dcm |
| By: | Fanchao Liao; Jaap Vleugel; Gustav B\"osehans; Dilum Dissanayake; Neil Thorpe; Margaret Bell; Bart van Arem; Gon\c{c}alo Homem de Almeida Correia |
| Abstract: | Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on their current travel mode. This study is based on stated preference data collected in Amsterdam. We analysed the data using mixed logit models. We found users of different modes not only have a varied general preference for different shared modes, but also have different sensitivity for attributes such as travel time and cost. Compared to car users, public transport users are more likely to switch towards the eHUBS modes. People who bike and walk have strong inertia, but the percentage choosing eHUBS modes doubles when the trip distance is longer (5 or 10 km). |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.19036&r=dcm |