Time series analysis of technical progress based on a Bayesian modeling
| Project/Area Number |
23730210
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Economic statistics
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| Research Institution | Tokyo University of Science (2013)
Yamagata University (2011-2012) |
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Principal Investigator |
NODA Hideo 東京理科大学, 経営学部, 准教授 (90347724)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2011: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 生産関数 / 技術進歩 / ベイズ法 / 平滑化事前分布 / TFP / 経済成長 / 景気変動 / 構造変化 |
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| Research Abstract |
In this paper, we propose a Bayesian approach for analyzing factor-augmenting technical changes based on a constant elasticity of substitution (CES) production function. To estimate trends in capital- and labor-augmenting technical change, a set of Bayesian linear models is constructed based on a smoothness prior approach. A statistical model constructed for the CES production function can then be expressed in a regression model with time-varying coefficients. However, the multicollinearity between the time-series data for the explanatory variables makes parameter estimation difficult. Therefore, we express the original model using two simplified models. Estimates of the parameters for each simplified model are obtained using Bayesian linear modeling and the maximum likelihood method. We then obtain a set of synthetic estimates for the time-varying coefficients based on Bayesian model averaging.
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(4 results)
Research Products
(41 results)
