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Nowcasting Norway

Author

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  • Matteo Luciani
  • Lorenzo Ricci

Abstract

We produce predictions of the previous, the current, and the next quarter of NorwegianGDP. To this end, we estimate a Bayesian Dynamic Factor model on a panel of 14variables (all followed closely by market operators) ranging from 1990 to 2011. By meansof a real time forecasting exercise we show that the Bayesian Dynamic Factor Model outperformsa standard benchmark model, while it performs equally well than the BloombergSurvey. Additionally, we use our model to produce annual GDP growth rate nowcast. Weshow that our annual nowcast outperform the Norges Bank’s projections of current yearGDP.

Suggested Citation

  • Matteo Luciani & Lorenzo Ricci, 2013. "Nowcasting Norway," Working Papers ECARES ECARES 2013-10, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/139866
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    Citations

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    Cited by:

    1. Antonello D’Agostino & Domenico Giannone & Michele Lenza & Michele Modugno, 2016. "Nowcasting Business Cycles: A Bayesian Approach to Dynamic Heterogeneous Factor Models," Advances in Econometrics,in: Dynamic Factor Models, volume 35, pages 569-594 Emerald Publishing Ltd.
    2. McDonald, Christopher & Thamotheram, Craig & Vahey, Shaun P. & Wakerly, Elizabeth C., 2015. "Assessing the Economic Value of Probabilistic Forecasts in the Presence of an Inflation Target," EMF Research Papers 09, Economic Modelling and Forecasting Group.
    3. Bragoli, Daniela & Modugno, Michele, 2017. "A now-casting model for Canada: Do U.S. variables matter?," International Journal of Forecasting, Elsevier, vol. 33(4), pages 786-800.
    4. Bragoli, Daniela, 2017. "Now-casting the Japanese economy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 390-402.
    5. Luciani, Matteo & Pundit, Madhavi & Ramayandi, Arief & Veronese, Giovanni, 2015. "Nowcasting Indonesia," Finance and Economics Discussion Series 2015-100, Board of Governors of the Federal Reserve System (U.S.).
    6. Christian Glocker & Philipp Wegmüller, 2017. "Business Cycle Dating and Forecasting with Real-time Swiss GDP Data," WIFO Working Papers 542, WIFO.
    7. repec:eee:intfor:v:33:y:2017:i:4:p:915-935 is not listed on IDEAS
    8. Delle Chiaie, Simona & Ferrara, Laurent & Giannone, Domenico, 2017. "Common factors of commodity prices," Working Paper Series 2112, European Central Bank.
    9. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    10. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-017-1254-1 is not listed on IDEAS
    11. Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2014. "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers.
    12. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    13. Alberto Caruso, 2015. "Nowcasting Mexican GDP," Working Papers ECARES ECARES 2015-40, ULB -- Universite Libre de Bruxelles.
    14. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    15. Dahlhaus, Tatjana & Guénette, Justin-Damien & Vasishtha, Garima, 2017. "Nowcasting BRIC+M in real time," International Journal of Forecasting, Elsevier, vol. 33(4), pages 915-935.
    16. Bok, Brandyn & Caratelli, Daniele & Giannone, Domenico & Sbordone, Argia M. & Tambalotti, Andrea, 2017. "Macroeconomic nowcasting and forecasting with big data," Staff Reports 830, Federal Reserve Bank of New York.

    More about this item

    Keywords

    real-time forecasting; bayesian factor model; nowcasting;

    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

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