【論文】

要旨：Spatial data are characterized by their spatial dependence, which is often complex, non-linear, and difficult to fully capture with a single model. Significant levels of model uncertainty– arising from these characteristics– cannot be resolved by model selection or simple ensemble methods, as performances are not homogeneous. We address this issue by proposing a novel methodology that captures spatially-varying model uncertainty, which we call Bayesian spatial predictive synthesis. Our proposal is defined by specifying a latent factor spatially-varying coefficient model as the synthesis function, which enables model coefficients to vary over the region to achieve flexible spatial model ensembling. We show that our proposal is derived from the theoretically best approximation of the data generating process and that it provides a finite sample theoretical guarantee for its predictive performance, specifically that the predictions are exact minimax. Two MCMC strategies are implemented for full uncertainty quantification, as well as a variational inference strategy for fast point inference. We also extend the estimation strategy for general responses. Through simulation examples and two real data applications, we demonstrate that our proposed Bayesian spatial predictive synthesis outperforms standard spatial models and ensemble methods, and advanced machine learning methods, in terms of predictive accuracy, while maintaining interpretability of the prediction mechanism.

Program

プログラム2022年度official

【論文】

要旨：Using panel data from 17 countries with varying economic circumstances from 1974 to 2019, we estimate regression models that explain residential property price dynamics by incorporating demographic factors and considering the interaction of those demographics with credit conditions. Our results show the importance of the demographic factors in modeling the long-run equilibrium of residential property prices. We find that the effect of nominal interest rates determined by monetary policy on asset prices varies depending on the country and the degree of population aging at the time. We also find that the persistently optimistic population projections lead to the oversupply of the residential stock in rapidly aging countries, resulting in stagnant residential property markets.

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《Paper》

Abstract: We develop an urban growth model where human capital spillovers foster entrepreneurship and learning in heterogeneous cities. Incumbent residents limit city expansion through planning regulations so that commuting and housing costs do not outweigh productivity gains from agglomeration. The model builds on strong microfoundations, matches key regularities at the city and economy-wide levels, and generates novel predictions for which we provide evidence. It can be quantified relying on few parameters, provides a basis to estimate the main ones, and remains transparent regarding its mechanisms. We examine various counterfactuals to assess the effect of cities on economic growth and aggregate output quantitatively.