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Data Revisions and the Output Gap

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  • Juan Manuel Julio
Abstract
Preliminary and delayed Colombian GDP reports are replaced with optimal in-sample now-casts of "true" GDP figures derived from a model for data revisions. The new GDP time series is augmented with optimal out-of-sample forecasts and back-casts of the "true" GDP figures derived from the same model. The trend-cycle component of the augmented GDP series is filtered. The resulting gap is more resistant than the ordinary HP filter to the end of sample optimal filtering problem as well as to GDP revisions and delays. The short term noise of the final output gap estimate is also reduced. Adjusting for data revisions and delays reduce the uncertainty of estimated gaps. The extended and further extended HP estimates of the output gap show an impressive efficiency gain with respect to the ordinary HP gap, 43% and 47% respectively, on average. The new extension increases the efficiency in 7.4%, on average, with respect to extended HP estimates. These results constitute a benchmark to future work on real time estimation of the output gap under GDP revisions and delays in Colombia.

Suggested Citation

  • Juan Manuel Julio, 2011. "Data Revisions and the Output Gap," Borradores de Economia 642, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:642
    DOI: 10.32468/be.642
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    References listed on IDEAS

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    Cited by:

    1. Amador-Torres, J. Sebastián, 2017. "Finance-neutral potential output: An evaluation in an emerging market monetary policy context," Economic Systems, Elsevier, vol. 41(3), pages 389-407.
    2. Julio Roman, Juan Manuel, 2011. "The Hodrick-Prescott filter with priors: linear restrictions on HP filters," MPRA Paper 34202, University Library of Munich, Germany.

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    More about this item

    Keywords

    Data Revisions; Now-casting; Real Time Economic Analysis.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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