TY - JOUR
T1 - Symbolic interval-valued data analysis for time series based on auto-interval-regressive models
AU - Lin, Liang Ching
AU - Chien, Hsiang Lin
AU - Lee, Sangyeol
N1 - Funding Information:
Funding was provided by Ministry of Science and Technology, Taiwan (Grant No. MOST 108-2118-M-006-010-MY2) and National Research Foundation of Korea (Grant No. No. 2018R1A2A2A05019433). Acknowledgements
Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
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U2 - 10.1007/s10260-020-00525-7
DO - 10.1007/s10260-020-00525-7
M3 - Article
AN - SCOPUS:85085967293
VL - 30
SP - 295
EP - 315
JO - Statistical Methods and Applications
JF - Statistical Methods and Applications
SN - 1618-2510
IS - 1
ER -