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Separating the impact of macroeconomic variables and global frailty in event data

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  • James Wolter

Abstract

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.

Suggested Citation

  • James Wolter, 2013. "Separating the impact of macroeconomic variables and global frailty in event data," Economics Series Working Papers 667, University of Oxford, Department of Economics.
  • Handle: RePEc:oxf:wpaper:667
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    File URL: http://www.economics.ox.ac.uk/materials/papers/12781/paper667.pdf
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    References listed on IDEAS

    as
    1. Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andr� Lucas, 2014. "Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 898-915, December.
    2. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    3. Darrell Duffie & Andreas Eckner & Guillaume Horel & Leandro Saita, 2009. "Frailty Correlated Default," Journal of Finance, American Finance Association, vol. 64(5), pages 2089-2123, October.
    4. Christopher Mayer & Karen Pence & Shane M. Sherlund, 2009. "The Rise in Mortgage Defaults," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 27-50, Winter.
    5. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
    6. Lando, David & Nielsen, Mads Stenbo, 2010. "Correlation in corporate defaults: Contagion or conditional independence?," Journal of Financial Intermediation, Elsevier, vol. 19(3), pages 355-372, July.
    7. Koopman, Siem Jan & Lucas, André & Schwaab, Bernd, 2011. "Modeling frailty-correlated defaults using many macroeconomic covariates," Journal of Econometrics, Elsevier, vol. 162(2), pages 312-325, June.
    8. Azizpour, Shahriar & Giesecke, Kay & Kim, Baeho, 2011. "Premia for correlated default risk," Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1340-1357, August.
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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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