Time series analysis of technical progress based on a Bayesian modeling

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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.