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Real-time Forecasts of Economic Activity for Latin American Economies

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Listed:
  • Philip Liu
  • Rafael Romeu
  • Troy D Matheson

Abstract

Macroeconomic policy decisions in real-time are based the assessment of current and future economic conditions. These assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates "nowcasts" and forecasts of real GDP growth using five alternative models for ten Latin American countries. The results indicate that the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.

Suggested Citation

  • Philip Liu & Rafael Romeu & Troy D Matheson, 2011. "Real-time Forecasts of Economic Activity for Latin American Economies," IMF Working Papers 11/98, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:11/98
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    References listed on IDEAS

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    1. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
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    11. Matheson, Troy D., 2010. "An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys," Economic Modelling, Elsevier, vol. 27(1), pages 304-314, January.
    12. K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze & G. Rünstler, 2008. "Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise," Working Paper Research 133, National Bank of Belgium.
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    14. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
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    Citations

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    Cited by:

    1. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    2. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    3. repec:eee:ecmode:v:66:y:2017:i:c:p:201-213 is not listed on IDEAS
    4. Luciani, Matteo & Pundit, Madhavi & Ramayandi, Arief & Veronese, Giovanni, 2015. "Nowcasting Indonesia," Finance and Economics Discussion Series 2015-100, Board of Governors of the Federal Reserve System (U.S.).
    5. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP
      [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]
      ," MPRA Paper 63713, University Library of Munich, Germany.
    6. repec:eee:intfor:v:33:y:2017:i:4:p:915-935 is not listed on IDEAS
    7. Germán López Espinosa, 2015. "Forecast Accuracy of Small and Large Scale Dynamic Factor Models in Developing Economies," Working Papers. Serie AD 2015-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    8. repec:eee:ecmode:v:66:y:2017:i:c:p:132-138 is not listed on IDEAS
    9. Dahlhaus, Tatjana & Guénette, Justin-Damien & Vasishtha, Garima, 2017. "Nowcasting BRIC+M in real time," International Journal of Forecasting, Elsevier, vol. 33(4), pages 915-935.
    10. Marcos Dal Bianco & Jaime Martinez-Martín & Maximo Camacho, 2013. "Short-Run Forecasting of Argentine GDP Growth," Working Papers 1314, BBVA Bank, Economic Research Department.

    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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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