A state-space model with eigenvector-based loadings and AR(1) factors for monthly default counts generates effective copulas and improved annual forecasts via temporal coarse-graining.
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Temporal Coarse-Graining of Multi-Sector Default Count Data Generates Posterior-Implied Copulas
A state-space model with eigenvector-based loadings and AR(1) factors for monthly default counts generates effective copulas and improved annual forecasts via temporal coarse-graining.