Simple Tests for the Correct Specification of Conditional Predictive Densities

Gergely Ganics and Lluc Puig Codina
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Gergely Ganics: BANCO DE ESPAÑA
Lluc Puig Codina: UNIVERSITY OF ALICANTE AND BANCO DE ESPAÑA

No 2535, Working Papers from Banco de España

Abstract: We propose a simplified framework for evaluating conditional predictive densities based on the probability integral transform (PIT). The approach accommodates a wide range of estimation schemes, including expanding and rolling windows, and applies to both stationary and non-stationary processes. By treating the PIT as a primitive, our approach enables researchers to apply widely used tests in settings where their validity was previously uncertain. Monte Carlo simulations demonstrate favorable size and power properties of the tests. In an empirical application, we show that incorporating stochastic volatility into an unobserved components model is essential for generating correctly calibrated density forecasts of US industrial production growth at both monthly and quarterly frequencies.

Keywords: predictive density; forecast evaluation; probability integral transform; Kolmogorov–Smirnov test; Cramér–von Mises test (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Pages: 71 pages
Date: 2035-09
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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:2535

DOI: 10.53479/40825

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