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Alternative tests for correct specification of conditional predictive densities



We propose a new framework for evaluating predictive densities in an eviroment where the estimation error of the parameters used to construct the densities is preserved asymptotically under the null hypothesis. The tests offer a simple way to evaluate the correct specification of predictive densities, where both the model specification and its estimation technique are evaluated jointly. Monte Carlo simulation results indicate that our tests are well sized and have good power in detecting misspecification. An empirical application to density forecasts of the Survey of Professional Forecasters shows the usefulness of our methodology.

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  • Barbara Rossi & Tatevik Sekhposyan, 2014. "Alternative tests for correct specification of conditional predictive densities," Economics Working Papers 1416, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2017.
  • Handle: RePEc:upf:upfgen:1416

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    References listed on IDEAS

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    Cited by:

    1. Adrian, Tobias & Boyarchenko, Nina & Giannone, Domenico, 2016. "Vulnerable Growth," CEPR Discussion Papers 11583, C.E.P.R. Discussion Papers.
    2. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Too many skew normal distributions? The practitioner’s perspective," Discussion Papers in Economics 13/07, Department of Economics, University of Leicester.

    More about this item


    Predictive Density; Probability Integral Transform; Kolmogorov-Smirnov Test; Cramér-von Mises Test; Forecast Evaluation;

    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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