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.