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Direct vs bottom–up approach when forecasting GDP: Reconciling literature results with institutional practice

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  • Esteves, Paulo Soares

Abstract

How should we forecast GDP? Should we forecast directly the overall GDP or aggregate the forecasts for each of its components using some level of disaggregation? The search for the answer continues to motivate several horse races between these two approaches. Nevertheless, independently of the results, institutions producing short-term forecasts usually opt for a bottom–up approach. This paper uses an application for the euro area to show that the option between direct and bottom–up approaches as the level of disaggregation chosen by forecasters is not determined by the results of those races.

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  • Esteves, Paulo Soares, 2013. "Direct vs bottom–up approach when forecasting GDP: Reconciling literature results with institutional practice," Economic Modelling, Elsevier, vol. 33(C), pages 416-420.
  • Handle: RePEc:eee:ecmode:v:33:y:2013:i:c:p:416-420
    DOI: 10.1016/j.econmod.2013.04.020
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    Citations

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    Cited by:

    1. Cobb, Marcus P A, 2017. "Forecasting Economic Aggregates Using Dynamic Component Grouping," MPRA Paper 81585, University Library of Munich, Germany.
    2. Francisco Craveiro Dias & Maximiano Pinheiro & António Rua, 2016. "A bottom-up approach for forecasting GDP in a data rich environment," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
    3. Marcus Cobb, 2014. "GDP Forecasting Bias due to Aggregation Inaccuracy in a Chain- Linking Framework," Working Papers Central Bank of Chile 721, Central Bank of Chile.
    4. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    5. Paulo Soares Esteves & António Rua, 2012. "Short-term forecasting for the portuguese economy: a methodological overview," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
    6. Cobb, Marcus P A, 2017. "Aggregate Density Forecasting from Disaggregate Components Using Large VARs," MPRA Paper 76849, University Library of Munich, Germany.
    7. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.

    More about this item

    Keywords

    GDP forecasts; Factor models; Bottom–up;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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