Inter-metropolitan regional migration galvanized by high-speed rail: A simulation analysis of the Linear Chuo Shinkansen line in Japan

The development of transportation infrastructure impacts migration, production, and other economic activities along with it. In this study, we conducted a multiregional computable general equilibrium simulation analysis to assess the effects that the proposed 2027 high-speed railway, the Linear Chuo Shinkansen in Japan, will have on population migration. The 47 prefectures ordained by the Japanese administrative unit were considered, and the simulation potential workforce population was taken as 110 million. The population is concentrated in the Tokyo metropolitan area. As per our simulation, once the proposed high-speed railway opens, “travel costs” (comprising time and the monetary costs of traveling) will decrease. This situation will stimulate economic activities and lead people to migrate to the prefectures where the economic environment improves. The results show that this development will ease extreme population concentration while simultaneously increasing employment and production values. However, additional analysis indicates that further development of the Linear Shinkansen to strengthen the current high-speed rail on the route to Tokaido will increase population concentration.     

Empirically-transformed linear opinion pools

The linear opinion pool (LOP) produces potentially non-Gaussian combination forecast densities. In this paper, we propose a computationally convenient transformation for the LOP to mirror the non-Gaussianity exhibited by the target variable. Our methodology involves a Smirnov transform to reshape the LOP combination forecasts using the empirical cumulative distribution function. We illustrate our empirically transformed opinion pool (EtLOP) approach with an application examining quarterly real-time forecasts for U.S. inflation evaluated on a sample from 1990:1 to 2020:2. EtLOP improves performance by approximately 10% to 30% in terms of the continuous ranked probability score across forecasting horizons.