Bayes Inference for Technological Substitution Data with Data-based Transformation
✍ Scribed by LYNN KUO; JACK LEE; PETER CHENG; JEFFREY PAI
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 232 KB
- Volume
- 16
- Category
- Article
- ISSN
- 0277-6693
No coin nor oath required. For personal study only.
✦ Synopsis
Bayesian inference via Gibbs sampling is studied for forecasting technological substitutions. The Box±Cox transformation is applied to the time series AR(1) data to enhance the linear model ®t. We compute Bayes point and interval estimates for each of the parameters from the Gibbs sampler. The unknown parameters are the regression coecients, the power in the Box±Cox transformation, the serial correlation coecient, and the variance of the disturbance terms. In addition, we forecast the future technological substitution rate and its interval. Model validation and model choice issues are also addressed. Two numerical examples with real data sets are given.