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4 - Survival analysis and omitted dividends

Published online by Cambridge University Press:  11 June 2010

By
Marc J. Leclere
Edited by
Stewart Jones  and
David A. Hensher
Stewart Jones
Affiliation:
University of Sydney
David A. Hensher
Affiliation:
University of Sydney
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Summary

Introduction

Survival analysis is a set of statistical methods designed for the analysis of time to event data. Its origins can be traced back to interests in population mortality in the late 1600s (e.g., see Graunt 1676) and its designation as ‘survival analysis’ reflects early applications in demography and biological science predominantly concerned with the ability of individuals or organisms to survive a given period of time until death. Although the use of survival analysis in the social sciences is fairly recent, the last ten years have seen an increase in the use of the method in economics-based research as researchers have begun to develop an interest in the duration of time that precedes the occurrence of an event.

Survival analysis models are concerned with examining the length of the time interval (‘duration’) between transition states (Blossfeld et al. 1989). The time interval is defined by an origin state and a destination state and the transition between the states is marked by the occurrence of an event during the observation period. An event is a qualitative change that occurs to an individual, organization, political party, society, or other collective (hereafter ‘individual’) as it changes from one discrete state to another discrete state as the result of a substantive process. The majority of survival analysis models examine the occurrence of a single event that transitions an individual across discrete states although there are models in which the event represents a transition to one of several states, repeated transitions from states, or where the event occurs many times.

Type
Chapter
Information
Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction , pp. 114 - 136
Publisher: Cambridge University Press
Print publication year: 2008

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