On sovereign credit migration: A study of alternative estimators and rating dynamics
Computational Statistics & Data Analysis
Unobserved heterogeneity in panel time series models
Computational Statistics & Data Analysis
Maximum likelihood estimation of an extended latent Markov model for clustered binary panel data
Computational Statistics & Data Analysis
Sieve bootstrap t-tests on long-run average parameters
Computational Statistics & Data Analysis
Evaluating probability of default: Intelligent agents in managing a multi-model system
Expert Systems with Applications: An International Journal
Structural model of credit migration
Computational Statistics & Data Analysis
Robust analysis of default intensity
Computational Statistics & Data Analysis
Multidimensional Distance-To-Collapse Point And Sovereign Default Prediction
International Journal of Intelligent Systems in Accounting and Finance Management
| Hi-index | 0.03 |
Sovereign default models that differ in their treatment of unobservable country, regional and time heterogeneities are systematically compared. The analysis is based on annual data over the 1983-2002 period for 96 developing economies. Inference-based criteria and parameter plausibility overwhelmingly favour more complex models that allow the link between the probability response and the fundamentals to vary over time and across countries. However, out-of-sample forecast evaluation using several loss functions and equal-predictive-ability tests suggests that simplicity beats complexity. Parsimonious pooled logit models produce the most accurate sovereign default forecasts and outperform the naive benchmarks.