Asymptotic theory on the least squares estimation of threshold moving-average models

Dong LI, Shiqing LING, Wai Keung LI

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12 Citations (Scopus)

Abstract

This paper studies the asymptotic theory of least squares estimation in a threshold moving average model. Under some mild conditions, it is shown that the estimator of the threshold is n-consistent and its limiting distribution is related to a two-sided compound Poisson process, whereas the estimators of other coefficients are strongly consistent and asymptotically normal. This paper also provides a resampling method to tabulate the limiting distribution of the estimated threshold in practice, which is the first successful effort in this direction. This resampling method contributes to threshold literature. Simultaneously, simulation studies are carried out to assess the performance of least squares estimation in finite samples. Copyright © 2012 Cambridge University Press.

Original languageEnglish
Pages (from-to)482-516
JournalEconometric Theory
Volume29
Issue number3
Early online dateNov 2012
DOIs
Publication statusPublished - Jun 2013

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simulation
performance
Moving average
Least squares
Asymptotic theory
Limiting distribution
Estimator
Resampling methods
literature
Simulation study
Coefficients
Finite sample
Compound Poisson process

Citation

Li, D., Ling, S., & Li, W. K. (2013). Asymptotic theory on the least squares estimation of threshold moving-average models. Econometric Theory, 29(3), 482-516. doi: 10.1017/S026646661200045X