Robust Implementation of a Parsimonious Dynamic Factor Model to Nowcast GDP

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CESifo Working Paper ; No. 4574

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models ; Forecasting Models; Simulation Methods
• Subject: small-scale nowcasting models ; Kalman Filter ; extreme bounds analysis

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Duarte, Pablo ; Süssmuth, Bernd
• Event: Veröffentlichung
• (who): Center for Economic Studies and ifo Institute (CESifo)
• (where): Munich
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/89720
• Last update: 10.03.2025, 11:43 AM CET