On Smooth Transition Interval Autoregressive Models

Bibliographic Data

ID19419192
AuthorsKai Yang (0000-0003-0493-9341, Changchun University of Technology), Yang Kai (0000-0002-6930-0285, Changchun University of Technology), Guangting Zhang (Changchun University of Technology), Dehui Wang (0000-0002-9185-9034, Liaoning University, corresponding author)
Year2026
Volume44
Issue2
Pages613-625
Publication date2026-04-03
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2025.2546455
OpenAlexW4413313686
LanguageEN
References cited31

Interval time series (ITS) analysis has important significance econometric analysis, as it contains information about the range of change and the level or trend of economic processes. More importantly, the rich information of interval data can be used for more accurate quantitative estimation and inference. Considering the possible nonlinear characteristics of ITS data, this article introduces a class of smooth transition interval autoregressive (STIAR) models, which includes the logistic STIAR (LSTIAR) model and the exponential STIAR (ESTIAR) model as special cases. The minimum distance estimation method is proposed to estimate the model parameters and the asymptotic theory of the estimator is established. The nonlinearity test of the model is also well solved. Finally, some numerical simulation results and a practical data example are given

Autoregressive integrated moving average · Autoregressive model · Combinatorics · Econometrics · Physics · SETAR · STAR model · Statistical physics · Statistics · Time series · Chemistry · Fault Detection and Control Systems · Market Dynamics and Volatility · Mathematics · Stock Market Forecasting Methods · Applied Mathematics

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