Symbolic interval-valued data analysis for time series based on auto-interval-regressive models

Liang Ching Lin, Hsiang Lin Chien, Sangyeol Lee

研究成果: Article同行評審

摘要

This study considers interval-valued time series data. To characterize such data, we propose an auto-interval-regressive (AIR) model using the order statistics from normal distributions. Furthermore, to better capture the heteroscedasticity in volatility, we design a heteroscedastic volatility AIR (HVAIR) model. We derive the likelihood functions of the AIR and HVAIR models to obtain the maximum likelihood estimator. Monte Carlo simulations are then conducted to evaluate our methods of estimation and confirm their validity. A real data example from the S&P 500 Index is used to demonstrate our method.

原文English
頁(從 - 到)295-315
頁數21
期刊Statistical Methods and Applications
30
發行號1
DOIs
出版狀態Published - 2021 三月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 統計、概率和不確定性

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