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.

Abstract: This study proposes a tying maximum likelihood estimation (TMLE) method to improve the performance of estimation of statistical and econometric models in which most time series have long sample periods, whereas the other time series are very short. The main idea of the TMLE is to tie the parameters of the long time series with those of the short time series together so that some useful information in the long time series which is related to the short time series can be transferred to the short time series. The information transferred from the long series can help improve the estimation accuracy of the parameters related to the short series. We first provide asymptotic properties of the TMLE and show its finite-sample risk bound with a fixed tuning parameter which determines the strength of tying. Further, we provide a method for selecting the tuning parameter based on a bootstrap procedure. A finite sample theory about this method is derived, which tells us how to conduct the bootstrap procedure effectively. Extensive artificial simulations and empirical applications show that the TMLE has an outstanding performance in point estimate and forecast.

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