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Filtered Likelihood for Point Processes

50 Pages Posted: 29 Jul 2011 Last revised: 10 Jun 2017

Kay Giesecke

Stanford University - Management Science & Engineering

Gustavo Schwenkler

Boston University - Questrom School of Business

Date Written: June 9, 2017

Abstract

Point processes are widely used in finance and economics to model the timing of defaults, market transactions, unemployment spells, births, and a range of other events. We develop and analyze likelihood estimators for the parameters of a marked point process and 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 illustrate the behavior of our estimators.

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

Suggested Citation

Giesecke, Kay and Schwenkler, Gustavo, Filtered Likelihood for Point Processes (June 9, 2017). 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 - Questrom School of Business ( email )

595 Commonwealth Ave
Boston, MA 02466
United States

HOME PAGE: http://www.gustavo-schwenkler.com

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