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on Discrete Choice Models |
| By: | Giovanni Compiani; Ilya Morozov; Stephan Seiler |
| Abstract: | We propose a demand estimation method that leverages unstructured text and image data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a random coefficients logit model. This approach enables researchers to estimate demand even when they lack data on product attributes or when consumers value hard-to-quantify attributes, such as visual design or functional benefits. Using data from a choice experiment, we show that our approach outperforms standard attribute-based models in counterfactual predictions of consumers' second choices. We also apply it across 40 product categories on Amazon and consistently find that text and image data help identify close substitutes within each category. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.20711 |
| By: | Masamune Iwasawa |
| Abstract: | Parameter estimation can result in substantial mean squared error (MSE), even when consistent estimators are used and the sample size is large. This paper addresses the longstanding statistical challenge of analyzing the bias and MSE of ridge-type estimators in nonlinear models, including duration, Poisson, and multinomial choice models, where theoretical results have been scarce. Employing a finite-sample approximation technique developed in the econometrics literature, this study derives new theoretical results showing that the generalized ridge maximum likelihood estimator (MLE) achieves lower finite-sample MSE than the conventional MLE across a broad class of nonlinear models. Importantly, the analysis extends beyond parameter estimation to model-based prediction, demonstrating that the generalized ridge estimator improves predictive accuracy relative to the generic MLE for sufficiently small penalty terms, regardless of the validity of the incorporated hypotheses. Extensive simulation studies and an empirical application involving the estimation of marginal mean and quantile treatment effects further support the superior performance and practical applicability of the proposed method. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.19018 |
| By: | Christopher En; Yuri Faenza |
| Abstract: | We show that all finite lattices, including non-distributive lattices, arise as stable matching lattices under standard assumptions on choice functions. In the process, we introduce new tools to reason on general lattices for optimization purposes: the partial representation of a lattice, which partially extends Birkhoff's representation theorem to non-distributive lattices; the distributive closure of a lattice, which gives such a partial representation; and join constraints, which can be added to the distributive closure to obtain a representation for the original lattice. Then, we use these techniques to show that the minimum cost stable matching problem under the same standard assumptions on choice functions is NP-hard, by establishing a connection with antimatroid theory. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.17916 |
| By: | Somdeb Lahiri |
| Abstract: | We interpret a fuzzy set as a random availability function and provide sufficient conditions under which a preference relation over the set of all random availability functions can be represented by a utility function. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.18863 |
| By: | Demian Pouzo; Martin Sola; Zacharias Psaradakis |
| Abstract: | This paper studies the robustness of quasi-maximum-likelihood (QML) estimation in hidden Markov models (HMMs) when the regime-switching structure is misspecified. Specifically, we examine the case where the true data-generating process features a hidden Markov regime sequence with covariate-dependent transition probabilities, but estimation proceeds under a simplified mixture model that assumes regimes are independent and identically distributed. We show that the parameters governing the conditional distribution of the observables can still be consistently estimated under this misspecification, provided certain regularity conditions hold. Our results highlight a practical benefit of using computationally simpler mixture models in settings where regime dependence is complex or difficult to model directly. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.21669 |
| By: | Appelbaum, Elie; Prisman, Eliezer, Z. |
| Abstract: | Hazard function applications in medical research are problematic for two reasons. First, they are not cast within a decision theory framework. Second, they often adopt severe self-imposed restrictive structures (e.g., in the constant hazard ratio models). The disadvantage of an excessively restrictive structure is self-evident. The disadvantage of the lack of a theoretical basis, which is more subtle, is that treatment choice itself becomes unnecessarily restrictive because decision theory insights remain untapped. This paper uses a decision-theory-based framework for treatment choice, thus addressing the two issues above. It shows that high-order stochastic dominance tools, in conjunction with risk preference attributes, can often be used to compare treatments under weaker conditions than the ones currently used. The paper compares treatments by using what we call high-order hazard functions. These high-order hazard functions are obtained by calculating areas under low-order hazard functions (such as standard and cumulative hazard functions). The paper provides necessary and sufficient conditions for treatment comparisons based on these high-order hazard functions. These conditions are shown to be weaker than the ones currently used because we are able to exploit theoretical tools that are otherwise unavailable. Thus, for example, it shows that our framework often allows treatment comparisons even when hazard functions cross. An example using real-world data shows that the use of high-order stochastic dominance and risk preference attributes allows us to identify a preferred treatment even if low-order hazard functions cross. |
| Keywords: | High-Order Hazard Functions, Treatment Choice, Survival Functions, High-Order Stochastic Dominance, Risk Preferences. |
| JEL: | C18 C65 D81 I0 I12 I19 |
| Date: | 2025–01–26 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124418 |
| By: | Edoardo Lombardo |
| Abstract: | We present here some results for the PDE related to the logHeston model. We present different regularity results and prove a verification theorem that shows that the solution produced via the Feynman-Kac theorem is the unique viscosity solution for a wide choice of initial data (even discontinuous) and source data. In addition, our techniques do not use Feller's condition at any time. In the end, we prove a convergence theorem to approximate this solution by means of a hybrid (finite differences/tree scheme) approach. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.19859 |
| By: | Minoru Kitahara; Yasunori Okumura |
| Abstract: | We examine a controlled school choice model where students are categorized into different types, and the distribution of these types within a school influences its priority structure. This study provides a general framework that integrates existing controlled school choice models, including those utilizing reserve rules, quota rules, and bonus-point rules. Specifically, we introduce an adjusted scoring rule that unifies these rules. By achieving a matching that satisfies the stability defined in this framework, matching authorities can effectively manage the trade-offs inherent in controlled school choice markets. Moreover, the priority order for a school is represented as a weak order with each given assignment, meaning that ties are allowed. Our mechanism ensures a stable matching and satisfies strategy-proofness. In particular, when priority orders are restricted to linear orders with each given assignment, our mechanism guarantees student-optimal stability. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.18220 |
| By: | Sebastian Bervoets; Mathieu Faure; Ludovic Renou |
| Abstract: | Deviations from Bayesian updating are traditionally categorized as biases, errors, or fallacies, thus implying their inherent ``sub-optimality.'' We offer a more nuanced view. We demonstrate that, in learning problems with misspecified models, non-Bayesian updating can outperform Bayesian updating. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.18024 |
| By: | Nielsen, Søren Bo (Dept. of Economics, Copenhagen Business School); Schindler, Dirk (Erasmus School of Economics, Erasmus University Rotterdam); Schjelderup, Guttorm (Dept. of Business and Management Science, Norwegian School of Economics) |
| Abstract: | We study how the OECD transfer pricing guidelines aimed at curbing tax-motivated transfer pricing practices affect investment incentives. Our theoretical model integrates the different OECD’s transfer pricing methods into the tax planning cost function of an MNC to evaluate how the choice of transfer price and quantity produced determine the amount of profit shifted. When the transfer pricing method used emphasizes the choice of transfer price over the choice of the quantity of the intermediate good, tax-motivated transfer pricing has positive investment effects. However, when the transfer pricing method treats profit shifting by price and quantity symmetrically, tax-motivated transfer pricing does not impact investment on the intensive margin. Our study has potential policy implications and also produces suggestions for empirical research on transfer pricing and investment. |
| Keywords: | Multinational corporations; corporate tax avoidance; transfer pricing; OECD transfer pricing rules; investment effects |
| JEL: | F23 H25 H26 M48 |
| Date: | 2025–05–22 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:nhhfms:2025_018 |
| By: | Daniel L. Chen (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | Ambiguity aversion is the interpretation of the experimental finding (the Ellsberg paradox) that most subjects prefer betting on events whose probabilities are known (objective) to betting on events whose probabilities are unknown (subjective). However in typical experiments these unknown probabilities are known by others. Thus the typical Ellsberg experiment is a situation of asymmetric information. People may try to avoid situations where they are the less informed party, which is normatively appropriate. We find that eliminating asymmetric information in the Ellsberg experiment while leaving ambiguity in place, makes subjects prefer the ambiguous bet over the objective one, reversing the prior results. |
| Keywords: | Uncertainty aversion, Probabilistic sophistication, Sources of ambiguity, Ellsberg paradox |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05012232 |
| By: | Lohawala, Nafisa (Resources for the Future); Arshad Rahman, Mohammad |
| Abstract: | The adoption of electric vehicles (EVs) is considered critical to achieving climate goals, yet it hinges on consumer interest. This study explores how public intent to purchase EVs relates to four unexamined factors (exposure to EV information, perceptions of EVs’ environmental benefits, views on government climate policy, and confidence in future EV infrastructure) while controlling for prior EV ownership, political affiliation, and demographic characteristics (age, gender, education, and geographic location). We use data from three nationally representative opinion polls by the Pew Research Center 2021 2023 and Bayesian techniques to estimate the ordinal probit and ordinal quantile models. Results from ordinal probit show that respondents who are well informed about EVs, perceive them as environmentally beneficial, or are confident in development of charging stations are more likely to express strong purchase interest, with covariate effects (CEs)−a metric rarely reported in EV research−of 10.2, 15.5, and 19.1 percentage points, respectively. In contrast, those skeptical of government climate initiatives are more likely to express no interest, by more than 10 percentage points. Prior EV ownership exhibits the highest CE (19.0–23.1 percentage points), and the impact of most demographic variables is consistent with the literature. The ordinal quantile models demonstrate significant variation in CEs across the distribution of purchase intent, offering insights beyond the ordinal probit model. We are the first to use quantile modeling to reveal how CEs differ significantly throughout the spectrum of purchase intent.Keywords: Decarbonization, electric vehicle, ordinal probit, Pew Research, quantile regression, technology adoption. |
| Date: | 2025–05–22 |
| URL: | https://d.repec.org/n?u=RePEc:rff:dpaper:dp-25-16 |
| By: | Yusuke Adachi |
| Abstract: | This paper develops a novel method for estimating the housing production function that addresses transmission bias caused by unobserved heterogeneity in land productivity. The approach builds on the nonparametric identification strategy of Gandhi et al. (2020) and exploits the zero-profit condition to allow consistent estimation even when either capital input or housing value is unobserved, under the assumption that land productivity follows a Markov process. Monte Carlo simulations demonstrate that the estimator performs well across a variety of production technologies. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.20429 |