Abstract

http://ssrn.com/abstract=1898344
 
 

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Citations (3)



 


 



Filtered Likelihood for Point Processes


Kay Giesecke


Stanford University - Management Science & Engineering

Gustavo Schwenkler


Boston University - Department of Finance & Economics

July 12, 2016


Abstract:     
Point processes are used to model the timing of defaults, market transactions, births, unemployment and many other events. We develop and study 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 establish an approximation to the likelihood and analyze the convergence and large-sample properties of the associated estimators. Numerical results highlight the computational efficiency of our estimators, and show that they can outperform EM Algorithm estimators.

Number of Pages in PDF File: 50

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


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Date posted: July 29, 2011 ; Last revised: July 13, 2016

Suggested Citation

Giesecke, Kay and Schwenkler, Gustavo, Filtered Likelihood for Point Processes (July 12, 2016). 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
Feedback to SSRN


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