Filtered Likelihood for Point Processes

50 Pages Posted: 29 Jul 2011 Last revised: 13 Jul 2016

Kay Giesecke

Stanford University - Management Science & Engineering

Gustavo Schwenkler

Boston University - Department of Finance & Economics

Date Written: 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.

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

Suggested Citation

Giesecke, Kay and Schwenkler, Gustavo, Filtered Likelihood for Point Processes (July 12, 2016). Available at SSRN: https://ssrn.com/abstract=1898344 or http://dx.doi.org/10.2139/ssrn.1898344

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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