Abstract Details
Activity Number:
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607
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #312695
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Title:
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Bias Reduction in Nonlinear and Dynamic Panels in the Presence of Cross-Section Dependence, with a GARCH Panel Application
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Author(s):
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Cavit Pakel*+
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Companies:
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Bilkent University
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Keywords:
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Incidental parameter bias ;
Nonlinear dynamic panels ;
Integrated likelihood method ;
Composite likelihood method ;
GARCH ;
Hedge funds
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Abstract:
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This study analyses the incidental parameter bias in non-linear and dynamic panel data models where the time-series and cross-section dimensions approach infinity at the same rate. The analysis focuses on the integrated likelihood method, allowing for dependence across both dimensions. I show that, although weak serial dependence leads to no extra bias, cross-section dependence generates a new type of bias, the magnitude of which depends on the strength of dependence. Likelihood-based analytical expressions for this bias term are provided under strong, weak and cluster-type cross-section dependence. Next, I consider the specific case of GARCH modelling using a panel of financial data. Simulation analysis reveals that the bias-corrected integrated likelihood method requires around 150 time-series observations to fit GARCH with little bias, compared to about 1,000 observations required for successful estimation by standard methods. Furthermore, the effect of cross-section dependence on bias is negligible, although it leads to higher variance. An empirical demonstration, analysing monthly hedge fund volatility characteristics is provided.
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Authors who are presenting talks have a * after their name.
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