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Real-time forecasting in a data-rich environment

  • Liebermann, Joelle

    (Central Bank of Ireland)

This paper assesses the ability of different models to forecast key real and nominal U.S.monthly macroeconomic variables in a data-rich environment from the perspective of a realtime forecaster, i.e. taking into account the real-time data revisions process and data fl ow. We find that for the real variables predictability is confined over the recent recession/crisis period. This is in line with the findings of D'Agostino and Giannone (2012) that gains in relative performance of models using large datasets over univariate models are driven by downturn periods which are characterized by higher comovements. Regarding in flation, results are stable across time, but predictability is mainly found at the very short-term horizons. In flation is known to be hard to forecast, but by exploiting timely information one obtains gains at nowcasting and forecasting one-month ahead, especially with Bayesian VARs. Furthermore, for both real and nominal variables, the direct pooling of information using a high dimensional model (dynamic factor model or Bayesian VAR) which takes into account the cross-correlation between the variables and efficiently deals with the "ragged edge" structure of the dataset, yields more accurate forecasts than the indirect pooling of bi-variate forecasts/models.

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Paper provided by Central Bank of Ireland in its series Research Technical Papers with number 07/RT/12.

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Date of creation: Dec 2012
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Handle: RePEc:cbi:wpaper:07/rt/12
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  1. Liebermann, Joelle, 2010. "Real-time nowcasting of GDP: Factor model versus professional forecasters," MPRA Paper 28819, University Library of Munich, Germany.
  2. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 27(2), pages 466-481, April.
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  4. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
  5. B. Jungbacker & S.J. Koopman & M. Van Der Wel, 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Post-Print hal-00828980, HAL.
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  9. Domenico Giannone & Martha Banbura & Lucrezia Reichlin, 2008. "Bayesian VARs with large panels," ULB Institutional Repository 2013/13388, ULB -- Universite Libre de Bruxelles.
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  24. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2008. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," Working Papers ECARES 2008_034, ULB -- Universite Libre de Bruxelles.
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  26. Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2010. "Short-Term Inflation Projections: a Bayesian Vector Autoregressive approach," CEPR Discussion Papers 7746, C.E.P.R. Discussion Papers.
  27. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-68, November.
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