標題: Bayes inference for technological substitution data with data-based transformation
作者: Kuo, L
Lee, J
Cheng, P
Pai, J
統計學研究所
Institute of Statistics
關鍵字: AR(1);Box-Cox transformation;Metropolis-within-Gibbs sampling;model choice;prediction
公開日期: 1-三月-1997
摘要: Bayesian inference via Gibbs sampling is studied for forecasting technological substitutions. The Box-Cox transformation is applied to the time series AR(I) data to enhance the linear model fit. We compute Bayes point and interval estimates for each of the parameters from the Gibbs sampler. The unknown parameters are the regression coefficients, the power in the Box-Cox transformation, the serial correlation coefficient, 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.
URI: http://hdl.handle.net/11536/14559
ISSN: 0277-6693
期刊: JOURNAL OF FORECASTING
Volume: 16
Issue: 2
起始頁: 65
結束頁: 82
顯示於類別:期刊論文


文件中的檔案:

  1. A1997WP38300001.pdf