Activity Number:
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65
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 12, 2002 : 8:30 AM to 10:20 AM
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Sponsor:
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Business & Economics Statistics Section*
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Abstract - #301148 |
Title:
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Simulation-Extrapolation Based Unit Root Tests for Stochastic Volatility
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Author(s):
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Kapil Sen*+ and Sastry Pantula
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Affiliation(s):
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North Carolina State University and North Carolina State Unviersity
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Address:
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, Raleigh, North Carolina, 27695,
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Keywords:
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stochastic volatility ; unit root ; measurement error ; simulation-extrapolation
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Abstract:
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The Stochastic Volatility Model (SVM) has emerged as a popular alternative to ARCH / GARCH models to analyze economic time series. In SVM, the observed series r_{t} is modeled as the product of an unobserved variance component and an independent error component. The unobserved volatility process is, in turn, modeled as an autoregressive process. We can test for a unit root in the volatility process by testing for a unit root in the log-squared of the observed process, log (r_{t}). Standard unit root tests performed on log (r_{t}) suffer from severe size distortions due to the presence of large negative moving average root in its autoregressive moving average representation. Cook and Stefanski (1994) developed the Simulation-Extrapolation (SIMEX) procedure as a simulation-based method of estimating and reducing bias due to measurement error in nonstandard generalized linear measurement error models. We propose to apply the SIMEX method to standard unit root tests based on ordinary least squares, weighted symmetric estimators and instrumental variables to correct the size distortion and still obtain sufficient power. Examples and extension to other processes will also be presented.
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