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Forecasting macroeconomic variables using disaggregate survey data

Author

Listed:
  • Kjetil Martinsen

    () (Norges Bank (Central Bank of Norway))

  • Francesco Ravazzolo

    () (Norges Bank (Central Bank of Norway))

  • Fredrik Wulfsberg

    (Norges Bank (Central Bank of Norway))

Abstract

We assess the forecast ability of Norges Bank’s regional survey for inflation, GDP growth and the unemployment rate in Norway. We propose several factor models based on regional and sectoral information given by the survey. The analysis identifies which information extracted from the ten sectors and the seven regions performs particularly well at forecasting different variables and horizons. Results show that several factor models beat an autoregressive benchmark in forecasting inflation and unemployment rate. However, the factor models are most successful in forecasting GDP growth. Forecast combinations based on past performance give in most cases more accurate forecasts than the benchmark, but they never give the most accurate forecasts.

Suggested Citation

  • Kjetil Martinsen & Francesco Ravazzolo & Fredrik Wulfsberg, 2011. "Forecasting macroeconomic variables using disaggregate survey data," Working Paper 2011/04, Norges Bank.
  • Handle: RePEc:bno:worpap:2011_04
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    File URL: http://www.norges-bank.no/en/Published/Papers/Working-Papers/2011/WP-201104/
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    References listed on IDEAS

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    Cited by:

    1. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," IREA Working Papers 201801, University of Barcelona, Research Institute of Applied Economics, revised Jan 2018.
    2. Alain Kabundi & Elmarie Nel & Franz Ruch, 2016. "Working Paper – WP/16/01- Nowcasting Real GDP growth in South Africa," Papers 7068, South African Reserve Bank.
    3. repec:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0002-5 is not listed on IDEAS
    4. Fornaro, Paolo, 2016. "Predicting Finnish economic activity using firm-level data," International Journal of Forecasting, Elsevier, vol. 32(1), pages 10-19.
    5. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    6. Kevin Moran & Simplice Aime Nono, 2016. "Using Confidence Data to Forecast the Canadian Business Cycle," Cahiers de recherche 1606, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    7. repec:taf:apeclt:v:24:y:2017:i:4:p:279-283 is not listed on IDEAS
    8. Knut Are Aastveit & Anne Sofie Jore & Francesco Ravazzolo, 2014. "Forecasting recessions in real time," Working Paper 2014/02, Norges Bank.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming”," AQR Working Papers 201706, University of Barcelona, Regional Quantitative Analysis Group, revised May 2017.
    10. R. Lehmann & K. Wohlrabe, 2017. "Experts, firms, consumers or even hard data? Forecasting employment in Germany," Applied Economics Letters, Taylor & Francis Journals, vol. 24(4), pages 279-283, February.
    11. repec:eee:intfor:v:33:y:2017:i:4:p:878-893 is not listed on IDEAS
    12. repec:spr:soinre:v:135:y:2018:i:1:d:10.1007_s11205-016-1490-3 is not listed on IDEAS
    13. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
    14. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.

    More about this item

    Keywords

    Keywords: Factor models; macroeconomic forecasting; qualitative survey data.;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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