Smooth buffered autoregressive time series models

Research output: Contribution to journalArticlespeer-review

Abstract

In this paper, we study a new model called the smooth buffered autoregressive (SBA) model. A sufficient condition is given for geometric ergodicity. A conditional least squares (CLS) estimation procedure is discussed, and consistency and normality of the estimators are derived. We investigate the effectiveness of our methods by simulation studies. Two applications are considered: annual sunspot number and the U.S. unemployment rate. Copyright © 2019 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)196-210
JournalJournal of Statistical Planning and Inference
Volume206
Early online dateOct 2019
DOIs
Publication statusPublished - May 2020

Citation

Lu, R., & Yu, P. L. H. (2020). Smooth buffered autoregressive time series models. Journal of Statistical Planning and Inference, 206, 196-210. doi: 10.1016/j.jspi.2019.09.012

Keywords

  • Conditional least squares
  • Geometric ergodicity
  • Threshold model

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