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

Liang Ching Lin, Hsiang Lin Chien, Sangyeol Lee

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)295-315
Number of pages21
JournalStatistical Methods and Applications
Volume30
Issue number1
DOIs
Publication statusPublished - 2021 Mar

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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