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Abstract Details
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Activity Number:
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309
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #301838 |
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Title:
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On the Approximate Maximum Likelihood Estimation for Diffusion Processes
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Author(s):
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Jinyuan Chang*+
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Companies:
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Peking University
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Address:
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Building 26, Room 224, Beijing, 100871, China
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Keywords:
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Asymptotic normality ;
Consistency ;
Discrete observation ;
Edgeworth expansion ;
Maximum likelihood estimation
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Abstract:
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The transitional density of a diffusion process is generally unknown, which prevents the full maximum likelihood estimation (MLE) based on discretely observed sample paths. A\"it-Sahalia (1999, 2002) proposed Edgeworth type series approximations to the transitional densities of diffusion processes, which lead to the approximate maximum likelihood estimation (AMLE) for parameters. The consistency and the rate of convergence of the AMLE are established, which reveal the roles played by the number of terms used in the density approximation and the sampling length between successive observations. We find conditions under which the AMLE have the same asymptotic distribution as that of the full MLE. A first order approximation to the Fisher information matrix is proposed.
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Authors who are presenting talks have a * after their name.
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