Threshold autoregressive models for interval-valued time series data

doi:10.1016/j.jeconom.2018.06.009

CSpace > 系统科学研究所

	Threshold autoregressive models for interval-valued time series data
	Sun, Yuying1,2 ; Han, Ai1,2 ; Hong, Yongmiao 3,4; Wang, Shouyang1,2
	2018-10-01
发表期刊	JOURNAL OF ECONOMETRICS
ISSN	0304-4076
卷号	206 期号:2 页码:414-446
摘要	Modeling and forecasting symbolic data, especially interval-valued time series (ITS) data, has received considerable attention in statistics and related fields. The core of available methods on ITS analysis is based on various applications of conventional linear modeling. However, few works have considered possible nonlinearities in ITS data. In this paper, we propose a new class of threshold autoregressive interval (TARI) models for ITS data. By matching the interval model with interval observations, we develop a minimum distance estimation method for TARI models, and establish the asymptotic theory for the proposed estimators. We show that the threshold parameter estimator is T-consistent and follows an asymptotic compound Poisson process as the sample size T -> infinity. And the estimators for other TARI model parameters are root-T consistent and asymptotically normal. Simulation studies show that the proposed TARI model provides more accurate out-of-sample forecasts than the existing center-radius self-exciting threshold (CR-SETAR) model for ITS data in the literature. Empirical applications to the S&P 500 Price Index document significant asymmetric reactions of the stock markets in Japan, U.K. and France to shocks from the U.S. stock market and that incorporating this asymmetric effect yield better out-of-sample forecasts than a variety of popular models available in the literature. (C) 2018 Published by Elsevier B.V.
关键词	Asymmetric reaction Interval-valued data Minimum distance estimation Nonlinearity Symbolic data Threshold autoregressive interval models
DOI	10.1016/j.jeconom.2018.06.009
语种	英语
资助项目	National Natural Science Foundation of China[7041100] ; National Natural Science Foundation of China[71671183]
WOS研究方向	Business & Economics ; Mathematics ; Mathematical Methods In Social Sciences
WOS类目	Economics ; Mathematics, Interdisciplinary Applications ; Social Sciences, Mathematical Methods
WOS记录号	WOS:000447571200008
出版者	ELSEVIER SCIENCE SA
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/31342
专题	系统科学研究所
通讯作者	Hong, Yongmiao
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China 3.Cornell Univ, Dept Econ, Ithaca, NY 14853 USA 4.Cornell Univ, Dept Stat Sci, Ithaca, NY 14850 USA
推荐引用方式 GB/T 7714	Sun, Yuying,Han, Ai,Hong, Yongmiao,et al. Threshold autoregressive models for interval-valued time series data[J]. JOURNAL OF ECONOMETRICS,2018,206(2):414-446.
APA	Sun, Yuying,Han, Ai,Hong, Yongmiao,&Wang, Shouyang.(2018).Threshold autoregressive models for interval-valued time series data.JOURNAL OF ECONOMETRICS,206(2),414-446.
MLA	Sun, Yuying,et al."Threshold autoregressive models for interval-valued time series data".JOURNAL OF ECONOMETRICS 206.2(2018):414-446.