Arbeitspapier
A dynamic factor model for nowcasting Canadian GDP growth
This paper estimates a dynamic factor model (DFM) for nowcasting Canadian gross domestic product. The model is estimated with a mix of soft and hard indicators, and it features a high share of international data. The model is then used to generate nowcasts, predictions of the recent past and current state of the economy. In a pseudo real-time setting, we show that the DFM outperforms univariate benchmarks as well as other commonly used nowcasting models, such as mixed-data sampling (MIDAS) and bridge regressions.
- Language
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Englisch
- Bibliographic citation
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Series: Bank of Canada Staff Working Paper ; No. 2017-2
- Classification
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Wirtschaft
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Forecasting Models; Simulation Methods
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Subject
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Econometric and statistical methods
Business fluctuations and cycles
- Event
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Geistige Schöpfung
- (who)
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Chernis, Tony
Sekkel, Rodrigo
- Event
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Veröffentlichung
- (who)
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Bank of Canada
- (where)
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Ottawa
- (when)
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2017
- DOI
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doi:10.34989/swp-2017-2
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Chernis, Tony
- Sekkel, Rodrigo
- Bank of Canada
Time of origin
- 2017
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