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Using the Flow of High Frequency Information for Short Term Forecasting of Economic Activity in Argentina

Author

Listed:
  • Laura D’Amato

    (Central Bank of Argentina)

  • Lorena Garegnani

    (Central Bank of Argentina)

  • Emilio Blanco

    (Central Bank of Argentina)

Abstract

We exploit the richness of a large data set of daily and monthly business cycle indicators by pooling them to produce Nowcast of contemporaneous real GDP growth. We conduct predictions based on a pooling of bivariate forecasts which uses these indicators as predictors of GDP (Nowcast with pooling). We also conduct a Nowcast exercise with factors for a restricted subset of business cycle indicators. When comparing the predictive accuracy of Nowcast with pooling and with factors with that of an AR(1) model, only the Nowcast with pooling outperforms the AR(1), indicating that the use of information released within the quarter helps to improve GDP growth prediction. The methodology then offers an encouraging and valuable approach to provide timely information for policy decision making.

Suggested Citation

  • Laura D’Amato & Lorena Garegnani & Emilio Blanco, 2011. "Using the Flow of High Frequency Information for Short Term Forecasting of Economic Activity in Argentina," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(64), pages 7-33, October -.
  • Handle: RePEc:bcr:ensayo:v:1:y:2011:i:64:p:7-33
    as

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    File URL: http://www.bcra.gov.ar/pdfs/investigaciones/64_Damato.pdf
    File Function: Spanish version (versión en Español)
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    References listed on IDEAS

    as
    1. Maurin, Laurent & Drechsel, Katja, 2008. "Flow of conjunctural information and forecast of euro area economic activity," Working Paper Series 925, European Central Bank.
    2. Katja Drechsel & Laurent Maurin, 2011. "Flow of conjunctural information and forecast of euro area economic activity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 336-354, April.
    3. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Laura D´Amato & Lorena Garegnani & Emilio Blanco, 2008. "Forecasting Inflation in Argentina: Individual Models or Forecast Pooling?," BCRA Working Paper Series 200835, Central Bank of Argentina, Economic Research Department.
    6. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    7. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, Elsevier.
    8. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, 06.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Deicy J. Cristiano & Manuel D. Hernández & José David Pulido, 2012. "Pronósticos de corto plazo en tiempo real para la actividad económica colombiana," Borradores de Economia 724, Banco de la Republica de Colombia.

    More about this item

    Keywords

    forecast pooling; forecast using a large dataset; nowcast; factor models;

    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
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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