Semi‐Parametric Specification Tests for Discrete Probability Models

Yue Fang

Journal of Risk & Insurance, 2003, vol. 70, issue 1, 73-84

Abstract: Loss functions play an important role in analyzing insurance portfolios. A fundamental issue in the study of loss functions involves the selection of probability models for claim frequencies. In this article, we propose a semi‐parametric approach based on the generalized method of moments (GMM) to solve the specification problems concerning claim frequency distributions. The GMM‐based testing procedure provides a general framework that encompasses many specification problems of interest in actuarial applications. As an alternative approach to the Pearson χ2 and other goodness‐of‐fit tests, it is easy to implement and should be of practical use in applications involving selecting and validating probability models with complex characteristics.

Date: 2003
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/1539-6975.00048

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jrinsu:v:70:y:2003:i:1:p:73-84

Ordering information: This journal article can be ordered from
http://www.wiley.com/bw/subs.asp?ref=0022-4367

Access Statistics for this article

Journal of Risk & Insurance is currently edited by Joan T. Schmit

More articles in Journal of Risk & Insurance from The American Risk and Insurance Association Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().