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Filtered likelihood for point processes

Kay Giesecke and Gustavo Schwenkler

Journal of Econometrics, 2018, vol. 204, issue 1, 33-53

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 processes; Filtering; Efficient parametric inference; Maximum likelihood; Likelihood approximation (search for similar items in EconPapers)
JEL-codes: C13 C32 C41 C58 C63 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:204:y:2018:i:1:p:33-53

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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