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

Hyper-parameterised dynamic regressions for nowcasting Spanish GDP growth in real time This paper analyses the nowcasting performance of hyperparameterised 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; co-movements; mixed estimation; prior elicitation; dynamic factor models; nowcasting plugin; JDemetra+. Reference to this paper should be made as follows: de Antonio Liedo, D. and Fernández Muñoz, E. (2017) `Hyper-parameterised dynamic regressions for nowcasting Spanish GDP growth in real time', Int. J. Computational Economics and Econometrics, Vol. 7, Nos. 1/2, pp.5­42. Biographical notes: David de Antonio Liedo holds a PhD in Economics at Université libre de Bruxelles and currently works at the Statistics Department of the National Bank of Belgium. After his experience as an Economist in institutions such as the Bank of Spain and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

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

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Publisher
Inderscience Publishers
Copyright
Copyright © 2017 Inderscience Enterprises Ltd.
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2017.080667
Publisher site
See Article on Publisher Site

Abstract

This paper analyses the nowcasting performance of hyperparameterised 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; co-movements; mixed estimation; prior elicitation; dynamic factor models; nowcasting plugin; JDemetra+. Reference to this paper should be made as follows: de Antonio Liedo, D. and Fernández Muñoz, E. (2017) `Hyper-parameterised dynamic regressions for nowcasting Spanish GDP growth in real time', Int. J. Computational Economics and Econometrics, Vol. 7, Nos. 1/2, pp.5­42. Biographical notes: David de Antonio Liedo holds a PhD in Economics at Université libre de Bruxelles and currently works at the Statistics Department of the National Bank of Belgium. After his experience as an Economist in institutions such as the Bank of Spain and

Journal

International Journal of Computational Economics and EconometricsInderscience Publishers

Published: Jan 1, 2017

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