ASYMPTOTIC THEORY ON THE LEAST SQUARES ESTIMATION OF THRESHOLD MOVING-AVERAGE MODELS

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 estimato...

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Vydáno v:Econometric theory Ročník 29; číslo 3; s. 482 - 516
Hlavní autoři: Li, Dong, Ling, Shiqing, Li, Wai Keung
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, USA Cambridge University Press 01.06.2013
Cambridge Univ. Press
Témata:
Asymptotic theory
Average
Coefficients
Consistent estimators
Distribution
Econometrics
Economic growth models
Economic models
Economic statistics
Economic theory
Ergodic theory
Estimating techniques
Estimation
Estimators
Euclidean space
GNP
Gross National Product
Least squares
Random variables
Simulation
Simulations
Statistical models
Studies
Thresholds
Time series
Tongs
ISSN:0266-4666, 1469-4360
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Shrnutí: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.
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ISSN:0266-4666
1469-4360
DOI:10.1017/S026646661200045X