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Filtered likelihood for point processes
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DOI：10.1016/j.jeconom.2017.11.011.png)
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Abstract
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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. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Point processes
Filtering
Efficient parametric inference
Maximum likelihood
Likelihood approximation
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