Abstract: Going beyond the New Economic Geography focus on progress in transportation cost, this paper introduces the dynamic effect of environmental technology on residential location in long-run spatial equilibrium. It develops a model with two regions in which a spatial public good (environmental quality) is degraded by externalities of differentiated private consumption goods, but degradation is abated by those of a single private environmental good. Producing the imperfectly tradable consumption goods requires both mobile workers and immobile workers, while the perfectly tradable environmental good requires only immobile workers. Mobile workers’ location choices are explained by regional disparities in environmental quality and price indexes, rather than in wages. Progress in transportation technology dynamically improves freeness of trade, but progress in environmental technology has the opposite effect. Dispersion occurs when progress in transportation technology dominates, while greater progress in abatement technology may lead to agglomeration.

17:00-17:45 “Parties’ Appeal and Asymmetric Elite Polarization” 尾崎夢輝 (京都大学)

17:45-18:30 “Conditional Value at Risk Maximizing Auction” 松下旦（京都大学）

Title: Scalable Estimation of Multinomial Response Models with Random Consideration Sets (with Siddhartha Chib)

Abstract: A common assumption in the fitting of unordered multinomial response models for J mutually exclusive categories is that the responses arise from the same set of J categories across subjects. However, when responses measure a choice made by the subject, it is more appropriate to condition the distribution of multinomial responses on a subject-specific consideration set, drawn from the power set of {1,2,…,J}. This leads to a mixture of multinomial response models governed by a probability distribution over the J* = 2^J -1 consideration sets. We introduce a novel method for estimating such generalized multinomial response models based on the fundamental result that any mass distribution over J* consideration sets can be represented as a mixture of products of J component specific inclusion-exclusion probabilities. Moreover, under time-invariant consideration sets, the conditional posterior distribution of consideration sets is sparse. These features enable a scalable MCMC algorithm for sampling the posterior distribution of parameters, random effects, and consideration sets. Under regularity conditions, the posterior distributions of the marginal response probabilities and the model parameters satisfy consistency. The methodology is demonstrated in a longitudinal data set on weekly cereal purchases that cover J = 101 brands, a dimension substantially beyond the reach of existing methods.

Title: The Estimation of Diffusion Processes with Private Network Information

Abstract: Innovations, either products or ideas, often spread through social networks. This paper introduces an econometric framework for identifying and estimating diffusion processes within these networks. Unlike traditional diffusion models that assume a continuous population with stochastic network structures, our approach examines scenarios where Bayesian agents observe their immediate neighbours within a persistent network. We establish the existence of a symmetric equilibrium and provide its characterization. Building upon these results, we propose a consistent and tractable two-step estimator for the payoff parameters, addressing endogeneity arising from exposure during the diffusion process. We evaluate the finite-sample performance of the estimator through Monte Carlo simulations and apply our method to the network data from Banerjee et al. (2013).

Abstract:
This paper provides novel nonparametric methods for estimation and uniform inference of optimal policy allocation rules and the resulting welfare. The original problem is formulated as a partially identified model, which entails non-standard asymptotics. We show that employing a surrogate risk function ensures point identification of a representing function, thereby enabling tractable asymptotic analysis based on Gaussian approximations. We propose a two-step cross-fitting procedure for estimating the nonparametric optimal policy, which achieves the standard nonparametric convergence rate and admits standard Gaussian approximations for inference. Leveraging these results and coupling principles, we develop a new method for nonparametric inference on both the optimal policy and the associated welfare. We also emphasize that the limiting distribution of the welfare estimator admits a Gaussian approximation, supported by the stochastic equicontinuity of the policy function estimator.