Filtered Likelihood for Point Processes

21 Pages Posted: 29 Jul 2011 Last revised: 8 Jun 2018

See all articles by Kay Giesecke

Kay Giesecke

Stanford University - Department of Management Science & Engineering

Gustavo Schwenkler

Santa Clara University - Department of Finance

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). Journal of Econometrics, Vol. 204, No. 1, 2018, Available at SSRN: https://ssrn.com/abstract=1898344 or http://dx.doi.org/10.2139/ssrn.1898344

Kay Giesecke (Contact Author)

Stanford University - Department of Management Science & Engineering ( email )

475 Via Ortega
Stanford, CA 94305
United States
(650) 723 9265 (Phone)
(650) 723 1614 (Fax)

HOME PAGE: http://https://giesecke.people.stanford.edu

Gustavo Schwenkler

Santa Clara University - Department of Finance ( email )

Santa Clara, CA 95053
United States

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