標題: ON ESTIMATION AND PREDICTION PROCEDURES FOR AR(1) MODELS WITH POWER TRANSFORMATION
作者: LEE, JC
TSAO, SL
統計學研究所
Institute of Statistics
關鍵字: AR(1) DEPENDENCE;BOX-COX TRANSFORMATION;MAXIMUM LIKELIHOOD;MINIMUM PREDICTION ERRORS;SIMULATIONS;TECHNOLOGY PENETRATION
公開日期: 1-八月-1993
摘要: The power transformation of Box and Cox (1964) has been shown to be quite useful in short-term forecasting for the linear regression model with AR(1) dependence structure (see, for example, Lee and Lu, 1987, 1989). It is crucial to have good estimates of the power transformation and serial. correlation parameters, because they form the basis for estimating other parameters and predicting future observations. The prediction of future observations is the main focus of this paper. We propose to estimate these two parameters by minimizing the mean squared prediction errors. These estimates and the corresponding predictions compare favourably, via revs and simulated data, with those obtained by the maximum likelihood method. Similar results are also demonstrated in the repeated measurements setting.
URI: http://dx.doi.org/10.1002/for.3980120604
http://hdl.handle.net/11536/14262
ISSN: 0277-6693
DOI: 10.1002/for.3980120604
期刊: JOURNAL OF FORECASTING
Volume: 12
Issue: 6
起始頁: 499
結束頁: 511
顯示於類別:期刊論文


文件中的檔案:

  1. A1993LU05800003.pdf