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Statistical Analysis of the Dynamic Structure of China's Economic Sectors Based on Bayesian Modeling OAK
Noda, Hideo; Kyo, Koki; 野田, 英雄; 姜, 興起.
This paper aims to develop an alternative production function-based approach for analyzing economic fluctuations at the sectoral level, by applying Bayesian techniques. To estimate total factor productivity (TFP) and elasticities of output with respect to factors of production, we incorporate smoothness priors into statistical models based on sectoral production functions. In addition, we consider that TFP generally varies smoothly; however in some situations there may be abrupt changes. Therefore, to relieve difficulties resulting from abrupt changes in TFP, a new method, termed the random grouping method, is introduced. Compared with the conventional production function approach, a main advantage of our proposed methods is to make detailed analysis of...
Palavras-chave: Bayesian modeling; Sectoral TFP; Smoothness priors; Random grouping method; Chinese economy.
Ano: 2010 URL: http://ir.obihiro.ac.jp/dspace/handle/10322/3126
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FIFTY YEARS OF REGIONAL INEQUALITY IN CHINA: A JOURNEY THROUGH REVOLUTION, REFORM AND OPENNESS AgEcon
Kanbur, Ravi; Zhang, Xiaobo.
This paper constructs and analyses a long run time series for regional inequality in China from the Communist Revolution to the present. There have been three peaks of inequality in the last fifty years, coinciding with the Great Famine of the late 1950s, the Cultural Revolution of the late 1960s and 1970s, and finally the period of openness and global integration in the late 1990s. Econometric analysis establishes that regional inequality is explained in the different phases by three key variables--the ratio of heavy industry to gross output value, the degree of centralization, and the degree of openness.
Tipo: Working or Discussion Paper Palavras-chave: Inequality; Polarisation; Decentralization; Industrialization; Openness; Globalization; Chinese economy; Political Economy; D63; 018; P27.
Ano: 2001 URL: http://purl.umn.edu/7236
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