Abstract Details
Activity Number:
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235
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #311106
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View Presentation
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Title:
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M-Estimation for General ARMA Processes with Infinite Variance
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Author(s):
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Rongning Wu*+
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Companies:
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Baruch College
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Keywords:
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ARMA process ;
bootstrap ;
infinite variance ;
M-estimation ;
non-causality ;
non-invertibility
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Abstract:
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General autoregressive moving average (ARMA) models extend the traditional ARMA models by removing the assumptions of causality and invertibility. The assumptions are not required under a non-Gaussian setting for the identifiability of the model parameters in contrast to the Gaussian setting. We study M-estimation for general ARMA processes with infinite variance, where the distribution of innovations is in the domain of attraction of a non-Gaussian stable law. Following the approach taken by Davis et al. (1992) and Davis (1996), we derive a functional limit theorem for random processes based on the objective function, and establish asymptotic properties of the M-estimator. We also consider bootstrapping the M-estimator and extend the results of Davis & Wu (1997) to the present setting so that statistical inferences are readily implemented. Simulation studies are conducted to evaluate the finite sample performance of the M-estimation and bootstrap procedures. An empirical example of financial time series is also provided.
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Authors who are presenting talks have a * after their name.
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