首页> 外文期刊>Journal of Forecasting >Forecasting time series with long memory and level shifts
【24h】

Forecasting time series with long memory and level shifts

机译:预测具有长记忆和电平移位的时间序列

获取原文
获取原文并翻译 | 示例
       

摘要

It is well known that some economic time series can be described by models which allow for either long memory or for occasional level shifts. In this paper we propose to examine the relative merits of these models by introducing a new model, which jointly captures the two features. We discuss representation and estimation. Using simulations, we demonstrate its forecasting ability, relative to the one-feature models, both in terms of point forecasts and interval forecasts. We illustrate the model for daily S&P500 volatility. Copyright (C) 2005 John Wiley Sons, Ltd.
机译:众所周知,一些经济时间序列可以通过模型来描述,这些模型既可以存储很长时间,也可以偶尔进行电平转换。在本文中,我们建议通过引入一个新的模型来研究这些模型的相对优点,该模型共同捕获了这两个特征。我们讨论表示和估计。通过仿真,我们从点预测和区间预测两个方面展示了其相对于一种功能模型的预测能力。我们说明了标准普尔500每日波动率的模型。版权所有(C)2005 John Wiley Sons,Ltd.

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
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