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
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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
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Objekttyp
- Arbeitspapier
Beteiligte
- Eraslan, Sercan
- Schröder, Maximilian
- Deutsche Bundesbank
Entstanden
- 2019