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Simple Tests for the Correct Specification of Conditional Predictive Densities

Author

Listed:
  • Gergely Ganics

    (BANCO DE ESPAÑA)

  • Lluc Puig Codina

    (UNIVERSITY OF ALICANTE AND 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.

Suggested Citation

  • Gergely Ganics & Lluc Puig Codina, 2035. "Simple Tests for the Correct Specification of Conditional Predictive Densities," Working Papers 2535, Banco de España.
    • Handle: RePEc:bde:wpaper:2535
    DOI: https://doi.org/10.53479/40825
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    References listed on IDEAS

    as
    1. Zhang, Bo & Chan, Joshua C.C. & Cross, Jamie L., 2020. "Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1318-1328.
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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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