Bayesian estimation of the CES production function with capital- and labour-augmenting technical change
by Hideo Noda; Koki Kyo
Asian J. of Management Science and Applications (AJMSA), Vol. 2, No. 1, 2015

Abstract: In this paper, we propose a Bayesian approach for analysing factor-augmenting technical changes based on a constant elasticity of substitution (CES) production function. To estimate trends in capital- and labour-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 modelling and the maximum likelihood method. We then obtain a set of synthetic estimates for the time-varying coefficients based on Bayesian model averaging. As a practical application, we examine technical changes in Taiwan and South Korea at the macroeconomic level. We find that labour-augmenting technical progress contributed greatly to Taiwan's economic growth during the period under investigation. On the other hand, in South Korea, the contribution of capital-augmenting technical change to economic growth was greater than that of labour-augmenting technical change. The results show that the proposed Bayesian approach can capture movements in technical change more rigorously than conventional approaches.

Online publication date: Tue, 22-Sep-2015

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