Separating the impact of macroeconomic variables and global frailty in event data
Global frailty is an unobserved macroeconomic variable.� In event data contexts, this unobserved variable is assumed to impact the hazard rate of event arrivals.� Attempts to identify and estimate the path of frailty are complicated when observed macroeconomic variables also impact hazard rates.� It is possible that the impact of the observed macro variables and global frailty can be confused and identification can fail.� In this paper I show that, under appropriate assumptions, the path of global frailty and the impact of observed macro variables can both be recovered.� This approach differs from previous work in that I do not assume frailty follows a specific stochastic process form.� Previous studies identify global frailty by assuming a stochastic form and using a filtering approach.� However, chosen stochastic forms are arbitrary and can potentially lead to poor results.� The method in this paper shows how to recover frailty without these assumptions.� This can serve as a model check to filtering�approaches.� The methods are applied to simulations and an application to corporate default.
|Date of creation:||10 Jul 2013|
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