Threshold Estimation via Group Orthogonal Greedy Algorithm

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

11 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)334-345
Journal / PublicationJournal of Business & Economic Statistics
Volume35
Issue number2
Online published13 Mar 2017
Publication statusPublished - Apr 2017
Externally publishedYes

Abstract

A threshold autoregressive (TAR) model is an important class of nonlinear time series models that possess many desirable features such as asymmetric limit cycles and amplitude-dependent frequencies. Statistical inference for the TAR model encounters a major difficulty in the estimation of thresholds, however. This article develops an efficient procedure to estimate the thresholds. The procedure first transforms multiple-threshold detection to a regression variable selection problem, and then employs a group orthogonal greedy algorithm to obtain the threshold estimates. Desirable theoretical results are derived to lend support to the proposed methodology. Simulation experiments are conducted to illustrate the empirical performances of the method. Applications to U.S. GNP data are investigated.

Research Area(s)

  • High-dimensional regression, Information criteria, Multiple-regime, Multiple-threshold, Nonlinear time series

Bibliographic Note

Publisher Copyright: © 2017 American Statistical Association.

Citation Format(s)

Threshold Estimation via Group Orthogonal Greedy Algorithm. / CHAN, Ngai Hang; ING, Ching-Kang; LI, Yuanbo et al.
In: Journal of Business & Economic Statistics , Vol. 35, No. 2, 04.2017, p. 334-345.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review