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Hyper-parameterised dynamic regressions for nowcasting Spanish GDP growth in real time

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  • David De Antonio Liedo
  • Elena Fernández Muñoz

Abstract

This paper analyses the nowcasting performance of hyper-parameterised dynamic regression models with a large number of variables in log levels, and compares it with state-of-the-art methods for nowcasting. We deal with the 'curse of dimensionality' by exploiting prior information originating in the Bayesian VAR literature. The real-time forecast simulation conducted over the most severe phase of the Great Recession shows that our method yields reliable GDP predictions almost one and a half months before the official figures are published. The usefulness of our approach is confirmed in a genuine out-of-sample evaluation over the European sovereign debt crisis and subsequent recovery.

Suggested Citation

  • David De Antonio Liedo & Elena Fernández Muñoz, 2017. "Hyper-parameterised dynamic regressions for nowcasting Spanish GDP growth in real time," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 5-42.
  • Handle: RePEc:ids:ijcome:v:7:y:2017:i:1/2:p:5-42
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