On the least squares estimation of threshold autoregressive and moving-average models

Dong LI, Wai Keung LI, Shiqing LING

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7 Citations (Scopus)

Abstract

This paper considers the least squares estimation and establishes its asymptotic theory for threshold autoregressive and moving-average models. Under some mild conditions, it is shown that the estimator of the threshold is n-consistent and after normalization it converges weakly to the smallest minimizer of a compound Poisson process, while the estimators of other coefficients are strongly consistent and asymptotically multivariate normal. This paper also provides a numerical method to tabulate the limiting distribution of the estimated threshold in practice. Simulation studies are carried out to assess the performance of the least squares estimation in finite samples. Copyright © 2011 International Press of Boston, Inc.
Original languageEnglish
Pages (from-to)183-196
JournalStatistics and its Interface
Volume4
Issue number2
DOIs
Publication statusPublished - 2011

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Moving Average Model
Least Squares Estimation
Estimator
Compound Poisson Process
Numerical methods
Multivariate Normal
Asymptotic Theory
Limiting Distribution
Minimizer
Normalization
Numerical Methods
Simulation Study
Converge
Coefficient

Citation

Li, D., Li, W. K., & Ling, S. (2011). On the least squares estimation of threshold autoregressive and moving-average models. Statistics and Its Interface, 4(2), 183-196. doi: 10.4310/SII.2011.v4.n2.a13

Keywords

  • Asymptotic normality
  • Compound Poisson process
  • Consistency
  • Least squares estimation
  • Threshold ARMA model