首页> 外文期刊>journal of time series analysis >AUTOMATIC SEMIPARAMETRIC ESTIMATION OF THE MEMORY PARAMETER OF A LONG‐MEMORY TIME SERIES
【24h】

AUTOMATIC SEMIPARAMETRIC ESTIMATION OF THE MEMORY PARAMETER OF A LONG‐MEMORY TIME SERIES

机译:AUTOMATIC SEMIPARAMETRIC ESTIMATION OF THE MEMORY PARAMETER OF A LONG‐MEMORY TIME SERIES

获取原文
           

摘要

Abstract.In Geweke and Porter‐Hudak's estimator ωd(GPH)of the memory parameter of a long‐memory process, a critical choice the user must make is the number of frequencies,M, to be used in the regression of the log periodogram on log frequency. This choice is critical in practice because by simply varyingMfor a given data set it is often possible to obtain a very wide range of values of the estimator. Although Geweke and Porter‐Hudak have found that choosingMto be the square root of the sample size gave good results in simulation, they gave no theoretical justification for this choice. Here, we propose automatic criteria for selectingM, and another tuning constant used in related estimates of the memory parameter, based on frequency domain cross‐validation. We provide some theoretical and heuristic justification for the proposed criteria. In a simulation study, we compare some of the resulting automatic methods of estimating the memory parameter with existing non‐auto

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号