Robust Implementation of a Parsimonious Dynamic Factor Model to Nowcast GDP

19 Pages Posted: 5 Feb 2014  

Pablo Duarte

University of Leipzig - Institute for Economic Policy

Bernd Suessmuth

University of Leipzig

Multiple version iconThere are 2 versions of this paper

Date Written: January 30, 2014

Abstract

Quarterly GDP figures usually are published with a delay of some weeks. A common way to generate GDP series of higher frequency, i.e. to nowcast GDP, is to use available indicators to calculate a single index by means of a common factor derived from a dynamic factor model (DFM). This paper deals with the implementation stage of this practice. We propose a two-tiered mechanism consisting in the identification of variables highly correlated with GDP as "core" indicators and a check of robustness of these variables in the sense of extreme bounds analysis. Accordingly selected indicators are used in an approximate DFM framework to exemplarily nowcast Spanish GDP growth. We show that our implementation produces more accurate nowcasts than both a benchmark stochastic process and the implementation based on the total set of core indicators.

Keywords: small-scale nowcasting models, Kalman Filter, extreme bounds analysis

JEL Classification: C380, C530

Suggested Citation

Duarte, Pablo and Suessmuth, Bernd, Robust Implementation of a Parsimonious Dynamic Factor Model to Nowcast GDP (January 30, 2014). CESifo Working Paper Series No. 4574. Available at SSRN: https://ssrn.com/abstract=2390583

Pablo Duarte

University of Leipzig - Institute for Economic Policy ( email )

Institute for Economic Policy
Grimmaische Straße 12
Leipzig, 04109
Germany

HOME PAGE: http://www.wifa.uni-leipzig.de/iwp/team1/duarte.html

Bernd Suessmuth (Contact Author)

University of Leipzig ( email )

Marschnerstrasse 31
D-04109 Leipzig, 04109
Germany

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