Hashiguchi, Yoshihiro (2009): Bayesian Estimation of Spatial Externalities Using Regional Production Function: The Case of China and Japan.
Preview |
PDF
MPRA_paper_17902.pdf Download (203kB) | Preview |
Abstract
This paper used regional panel data for Chinese provinces from 1979 to 2003, and for Japanese prefectures from 1955 to 1998, to estimate the spatial externalities (or spatial multiplier effects) using a production function and Bayesian methodology, and to investigate the long-run behavior of the spatial externalities of each country. According to the estimation results, China's spatial externalities increased its domestic production significantly after 1994, which tended to increase until 2003. Before 1993, however, its spatial externalities were not significant. Japan's spatial externalities showed fluctuating values throughout the sample period. Furthermore, the movement of the spatial externalities was correlated with Japan's business conditions: the externalities showed a high value in the economic boom, and a low value in the economic depression. This could mean that spatial externalities depend mainly on business conditions.
Item Type: | MPRA Paper |
---|---|
Original Title: | Bayesian Estimation of Spatial Externalities Using Regional Production Function: The Case of China and Japan |
Language: | English |
Keywords: | Spatial Externalities; Bayesian Estimation; Production Function |
Subjects: | E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E23 - Production N - Economic History > N9 - Regional and Urban History > N95 - Asia including Middle East C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General |
Item ID: | 17902 |
Depositing User: | Yoshihiro Hashiguchi |
Date Deposited: | 16 Oct 2009 07:10 |
Last Modified: | 28 Sep 2019 16:41 |
References: | Anselin L. 1988. Spatial Econometrics: Methods and Models. Dordrecht: Kluwer. Anselin L. 2001. “Spatial Econometrics.” in A Companion to Theoretical Econometrics, Baltagi B. (eds). Malden Mass: Blackwell Publishers; 310–330. Anselin L. 2003. “Spatial Externalities, Spatial Multipliers, and Spatial Econometrics.” International Regional Science Review 26: 153–166. Doi T. 2002. Chiiki kara mita Nihon Keizai to Zaiseiseisaku (Japanese Economy and Fiscal Policy Seen from Regions). Tokyo: The Mitsubisi Economic Research Institute. In Japanese. Doornik JA. 2006. Ox: An Object-Oriented Matrix Programming Language. London: Timberlake Consultants. Durbin J, Koopman SJ. 2002. “A Simple and Efficient Simulation Smoother for State Space Time Series Analysis.” Biometrika 89: 603–615. Ertur C, Koch W. 2007. “Growth, Technological Interdependence and Spatial Externalities: Theory and Evidence.” Journal of Applied Econometrics 22: 1033–1062. Fingleton B, Lopez-Bazo E. 2006. “Empirical Growth Models with Spatial Effects.” Papers in Regional Science 85: 177–198. Hashiguchi Y, Chen K. 2006. “Chuugoku no Shoubetsu Shihon Stock no Suikei: Ezaki-Son no Houhou to Daitaiteki Houhou (Estimating China’s Provincial Capital Stocks: Ezaki and Sun’s Method and an Alternative).” Kokumin Keizai Zasshi (Journal of political economy and commercial science) 193(6): 73–86. In Japanese. Heston A, Summers R, Aten B. 2002. Penn World Tables Version 6.1. Downloadable Data Set. Center for International Comparisons of Production, Income and Prices (CIC), University of Pennsylvania. Kakamu K, Polasek W, Wago H. 2007. “Spatial Agglomeration and Spill-over Analysis for Japanese Prefectures During 1991–2000.” MODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 958–964. Kato H, Chen K. 2002. China, East Asian Long-Term Econometric Data Ser. 12. Tokyo Iwanami Shoten. In Japanese. Koopman SJ, Shephard N, Doornik JA. 1999. “Statistical Algorithms for Models in State Space Using SsfPack 2.2.” Econometrics Journal 2: 107–160. Olejnik A. 2008. “Using the Spatial Autoregressively Distributed Lag Model in Assessing the Regional Convergence of Per-capita Income in the EU25.” Papers in Regional Science 87: 371–384. Pfaffermayr M. 2009. “Conditional b- and s-Convergence in Space: A Maximum Likelihood Approach.” Regional Science and Urban Economics 39: 63–78. Vaya E, Lopez-Bazo E, Moreno R, Surinach J. 2004. “Growth and Externalities Across Economies: An Empirical Analysis Using Spatial Econometrics.” in Advances in Spatial Econometrics: Methodology, Tools and Applications, Anselin L, Florax RJGM, Rey SJ (eds). Berlin: Springer; 433–455. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/17902 |