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    Forecasting substantial data revisions in the presence of model uncertainty

    Garratt, Anthony and Koop, G. and Vahey, S.P. (2006) Forecasting substantial data revisions in the presence of model uncertainty. Working Paper. Birkbeck, University of London, London, UK.

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    Abstract

    A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable press attention, provoked a public enquiry and prompted a number of reforms to UK statistical reporting procedures. In this paper, we compute the probability of “substantial revisions” that are greater (in absolute value) than the controversial 2003 revision. The predictive densities are derived from Bayesian model averaging over a wide set of forecasting models including linear, structural break and regime-switching models with and without heteroskedasticity. Ignoring the nonlinearities and model uncertainty yields misleading predictives and obscures recent improvements in the quality of preliminary UK macroeconomic measurements.

    Metadata

    Item Type: Monograph (Working Paper)
    Additional Information: BWPEF 0617
    Keyword(s) / Subject(s): Revisions, Structural Breaks, Regime Switching, Model Uncertainty, Bayesian Model Averaging, Predictive Densities
    School: School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Administrator
    Date Deposited: 28 Mar 2019 06:31
    Last Modified: 13 Jun 2021 16:24
    URI: https://eprints.bbk.ac.uk/id/eprint/26926

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