Arbeitspapier
Forecasting with a noncausal VAR model
We propose simulation-based forecasting methods for the noncausal vector autoregressive model proposed by Lanne and Saikkonen (2012). Simulation or numerical methods are required because the prediction problem is generally nonlinear and, therefore, its analytical solution is not available. It turns out that different special cases of the model call for different simulation procedures. Simulation experiments demonstrate that gains in forecasting accuracy are achieved by using the correct noncausal VAR model instead of its conventional causal counterpart. In an empirical application, a noncausal VAR model comprised of U.S. inflation and marginal cost turns out superior to the bestfitting conventional causal VAR model in forecasting inflation.
- ISBN
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978-952-462-828-0
- Language
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Englisch
- Bibliographic citation
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Series: Bank of Finland Research Discussion Papers ; No. 33/2012
- Classification
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Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Forecasting Models; Simulation Methods
- Subject
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Noncausal vector autoregression
forecasting
simulation
importance sampling
inflation
- Event
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Geistige Schöpfung
- (who)
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Nyberg, Henri
Saikkonen, Pentti
- Event
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Veröffentlichung
- (who)
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Bank of Finland
- (where)
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Helsinki
- (when)
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2012
- 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
- Nyberg, Henri
- Saikkonen, Pentti
- Bank of Finland
Time of origin
- 2012
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