Arbeitspapier
Implementing an approximate dynamic factor model to nowcast GDP using sensitivity analysis
Dynamic factor models based on Kalman Filter techniques are frequently used to nowcast GDP. This study deals with the selection of indicators for this practice. We propose a two-tiered mechanism which is shown in a case study to produce more accurate nowcasts than a benchmark stochastic process and a standard model including extreme bounds fragile indicators. Nowcasting accuracy nearly measures up to the one of real-time forecasts by an institution with an interest in high-quality nowcasts.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 152
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
Forecasting Models; Simulation Methods
- Thema
-
dynamic factor
Kalman Filter
extreme bounds analysis
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Duarte, Pablo
Süßmuth, Bernd
- Ereignis
-
Veröffentlichung
- (wer)
-
Universität Leipzig, Wirtschaftswissenschaftliche Fakultät
- (wo)
-
Leipzig
- (wann)
-
2018
- 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.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Duarte, Pablo
- Süßmuth, Bernd
- Universität Leipzig, Wirtschaftswissenschaftliche Fakultät
Entstanden
- 2018
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