Abstract

http://ssrn.com/abstract=1898344
 
 

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Filtered Likelihood for Point Processes


Kay Giesecke


Stanford University - Management Science & Engineering

Gustavo Schwenkler


Boston University - Department of Finance & Economics

July 28, 2011


Abstract:     
We develop likelihood estimators of the parameters of a marked point process and of incompletely observed explanatory factors that influence the arrival intensity and mark distribution. We provide conditions guaranteeing consistency and asymptotic normality as the sample period grows. We also establish an approximation to the likelihood and analyze the convergence and asymptotic properties of the associated estimators. Numerical results illustrate our approach.

Number of Pages in PDF File: 41

Keywords: point process, filtering, parametric maximum likelihood, asymptotic theory, likelihood approximation

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Date posted: July 29, 2011 ; Last revised: November 7, 2014

Suggested Citation

Giesecke, Kay and Schwenkler, Gustavo, Filtered Likelihood for Point Processes (July 28, 2011). Available at SSRN: http://ssrn.com/abstract=1898344 or http://dx.doi.org/10.2139/ssrn.1898344

Contact Information

Kay Giesecke (Contact Author)
Stanford University - Management Science & Engineering ( email )
473 Via Ortega
Stanford, CA 94305-9025
United States
(650) 723 9265 (Phone)
(650) 723 1614 (Fax)
HOME PAGE: http://www.stanford.edu/~giesecke/
Gustavo Schwenkler
Boston University - Department of Finance & Economics ( email )
595 Commonwealth Avenue
Boston, MA 02215
United States
HOME PAGE: http://people.bu.edu/gas
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