Nowcasting Norway

Author
Listed:
Matteo Luciani
(ECARES, SBS-EM, Université libre de Bruxelles, F.R.S.-FNRS)
Lorenzo Ricci
(ECARES, SBS-EM, Université libre de Bruxelles)
Abstract
We produce predictions of Norwegian GDP. To this end, we estimate a Bayesian dynamic factor model on a panel of fourteen variables (all followed closely by market operators) ranging from 1990 to 2011. By means of a pseudo real-time exercise, we show that the Bayesian dynamic factor model performs well both in terms of point forecast and in terms of density forecasts. Results indicate that our model outperforms standard univariate benchmark models, that it performs as well as the Bloomberg survey, and that it outperforms the predictions published by the Norges Bank in its Monetary Policy Report.
Suggested Citation
Matteo Luciani & Lorenzo Ricci, 2014.
Nowcasting Norway
,"
International Journal of Central Banking
, International Journal of Central Banking, vol. 10(4), pages 215-248, December.
Handle:
RePEc:ijc:ijcjou:y:2014:q:4:a:7
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More about this item
JEL
classification:
C32
- Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
C53
- Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
E37
- Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
Statistics
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