This paper applies Bayesian modeling techniques to analyze trends in total factor productivity (TFP) behind Taiwan's economic growth. To estimate time-varying TFP and elasticities of output with respect to factors of production, smoothness priors are incorporated into statistical models based on a Cobb-Douglas production function. We assume that in some situations there may be rapid changes in TFP, although TFP in general varies smoothly. Our modeling enables the expression of diverse patterns of TFP and an understanding of the continuous movement of TFP. Thus, a more flexible model can be constructed compared with the conventional models in empirical studies of TFP. The results suggest that TFP will play an increasingly important role in Taiwan's future economic growth.
|Number of pages||8|
|Journal||ICIC Express Letters, Part B: Applications|
|Publication status||Published - 4 Sep 2013|
- Bayesian modeling
- Economic growth
- Smoothness priors
- Total factor productivity