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

David de Antonio Liedo and Elena Fernández Muñoz

International Journal of Computational Economics and Econometrics, 2017, vol. 7, issue 1/2, 5-42

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.

Keywords: Bayesian shrinkage; VAR; value-at-risk; co-movements; mixed estimation; prior elicitation; dynamic factor models; nowcasting plugin; JDemetra+; hyper-parameterised dynamic regressions; Spain; GDP growth; gross domestic product; real time; modelling; curse of dimensionality; simulation; European sovereign debt crisis; GDP forecasting. (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:7:y:2017:i:1/2:p:5-42

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