Arbeitspapier

Nowcasting GDP with a large factor model space

We propose a novel time-varying parameters mixed-frequency dynamic factor model which is integrated into a dynamic model averaging framework for macroeconomic nowcasting. Our suggested model can efficiently deal with the nature of the real-time data flow as well as parameter uncertainty and time-varying volatility. In addition, we develop a fast estimation algorithm. This enables us to generate nowcasts based on a large factor model space. We apply the suggested framework to nowcast German GDP. Our recursive out-of-sample forecast evaluation results reveal that our framework is able to generate forecasts superior to those obtained from a naive and more competitive benchmark models. These forecast gains seem to emerge especially during unstable periods, such as the Great Recession, but also remain over more tranquil periods.

ISBN
978-3-95729-641-2
Sprache
Englisch

Erschienen in
Series: Deutsche Bundesbank Discussion Paper ; No. 41/2019

Klassifikation
Wirtschaft
Bayesian Analysis: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Construction and Estimation
Model Evaluation, Validation, and Selection
Forecasting Models; Simulation Methods
Thema
dynamic factor model
forecasting
GDP
mixed-frequency
model averaging
time-varying-parameter

Ereignis
Geistige Schöpfung
(wer)
Eraslan, Sercan
Schröder, Maximilian
Ereignis
Veröffentlichung
(wer)
Deutsche Bundesbank
(wo)
Frankfurt a. M.
(wann)
2019

Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Eraslan, Sercan
  • Schröder, Maximilian
  • Deutsche Bundesbank

Entstanden

  • 2019

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