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D. Cox (1972)
Regression models and life tables (with discussion
L. George (2003)
The Statistical Analysis of Failure Time DataTechnometrics, 45
C. Struthers, J. Kalbfleisch (1986)
Misspecified proportional hazard modelsBiometrika, 73
S. Chava, C. Stefanescu, S. Turnbull (2011)
Modeling the Loss DistributionManag. Sci., 57
P. Rousseeuw, A. Leroy (2005)
Wiley Series in Probability and Mathematical Statistics
(1966)
Financial ratios and the prediction of failure
H. White (1982)
Maximum Likelihood Estimation of Misspecified ModelsEconometrica, 50
O. Aalen, O. Borgan, H. Gjessing (2008)
Survival and Event History Analysis: A Process Point of View
I. McKeague, P. Sasieni (1994)
A partly parametric additive risk modelBiometrika, 81
Stephen Figlewski, H. Frydman, W. Liang (2006)
Modeling the Effect of Macroeconomic Factors on Corporate Default and Credit Rating TransitionsMacroeconomics eJournal
S. Self, R. Prentice (1982)
Commentary on Andersen and Gill's "Cox's Regression Model for Counting Processes: A Large Sample Study"Annals of Statistics, 10
S. Davydenko (2012)
When Do Firms Default? A Study of the Default BoundaryS&P Global Market Intelligence Research Paper Series
Sanjiv Das, D. Duffie, Nikunj Kapadia, Leandro Saita (2006)
Common Failings: How Corporate Defaults are CorrelatedLSN: Law & Finance: Empirical (Topic)
W. Barlow, R. Prentice (1988)
Residuals for relative risk regressionBiometrika, 75
H. Ramlau-Hansen (1983)
Smoothing Counting Process Intensities by Means of Kernel FunctionsAnnals of Statistics, 11
O. Aalen, J. Fosen, H. Weedon-Fekjær, Ørnulf Borgan, E. Husebye (2004)
Dynamic Analysis of Multivariate Failure Time DataBiometrics, 60
D. Duffie, Leandro Saita, Ke Wang (2005)
Multi-Period Corporate Default Prediction with Stochastic CovariatesMacroeconomics eJournal
Tim Robertson, R. Dykstra, F. Wright (1988)
Order restricted statistical inference
J. Chen, Handout (2017)
Probability and Mathematical Statistics
O. Aalen (1989)
A linear regression model for the analysis of life times.Statistics in medicine, 8 8
T. Martinussen, T. Scheike (2006)
Dynamic Regression Models for Survival Data
(2011)
Development Core Team
F. Couderc, O. Renault, F. Couderc, O. Renault, De Servigny, O. Scaillet, Rene Stulz, Laurent Barras
Fame -international Center for Financial Asset Management and Engineering Global and Industry Cycle Times-to-default: Life Cycle, Global and Industry Cycle Impacts Financial Valuation and Risk Management (nccr Finrisk) and from the Geneva Research Collaboration Foundation (grc). We Thank Arnaud
R. Team (2014)
R: A language and environment for statistical computing.MSOR connections, 1
D. Zucker, A. Karr (1990)
Nonparametric Survival Analysis with Time-Dependent Covariate Effects: A Penalized Partial Likelihood ApproachAnnals of Statistics, 18
(2006)
Macroeconomic effect in corporate default
A. Le (2007)
Separating the Components of Default Risk:A Derivative-Based ApproachSPGMI: Compustat Fundamentals (Topic)
J. Kalbfleisch, R. Prentice (1980)
The Statistical Analysis of Failure Time Data
(2001)
Forecasting bankruptcy more efficiently : A simple hazard model
D. Lando, M. Nielsen (2008)
Correlation in Corporate Defaults: Contagion or Conditional Independence?Corporate Finance: Governance
D. Lando, Torben Skødeberg (2002)
Analyzing rating transitions and rating drift with continuous observationsJournal of Banking and Finance, 26
T. Mikosch, A. Vaart, J. Wellner (1996)
Weak Convergence and Empirical Processes: With Applications to Statistics
D. Duffie, Guillaume Horel, Leandro Saita, Andreas Eckner (2006)
Frailty Correlated DefaultERN: Bayesian Analysis (Topic)
Peter Fledelius, D. Lando, J. Nielsen (2004)
Non-Parametric Analysis of Rating Transition and Default Data
6. Smoothed regression coefficients
We consider additive intensity (Aalen) models as an alternative to the multiplicative intensity (Cox) models for analyzing the default risk of a sample of rated, nonfinancial U.S. firms. The setting allows for estimating and testing the significance of time-varying effects. We use a variety of model checking techniques to identify misspecifications. In our final model, we find evidence of time-variation in the effects of distance-to-default and short-to-long term debt. Also we identify interactions between distance-to-default and other covariates, and the quick ratio covariate is significant. None of our macroeconomic covariates are significant.
Journal of Financial Econometrics – Oxford University Press
Published: Jun 10, 2013
Keywords: JEL G32 G33 C41 C52
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