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Optimal Combination of Survey Forecasts

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  • Conflitti, Cristina
  • De Mol, Christine
  • Giannone, Domenico

Abstract

We consider the problem of optimally combining individual forecasts of gross domestic product (GDP) and inflation from the Survey of Professional Forecasters (SPF) dataset for the Euro Area. Contrary to the common practice of using equal combination weights, we compute optimal weights which minimize the mean square forecast error (MSFE) in the case of point forecasts and maximize a logarithmic score in the case of density forecasts. We show that this is a viable strategy even when the number of forecasts to combine gets large, provided we constrain these weights to be positive and to sum to one. Indeed, this enforces a form of shrinkage on the weights which ensures good out-of-sample performance of the combined forecasts.

Suggested Citation

  • Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2012. "Optimal Combination of Survey Forecasts," CEPR Discussion Papers 9096, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:9096
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    Citations

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    Cited by:

    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    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. repec:eee:renene:v:116:y:2018:i:pa:p:669-684 is not listed on IDEAS
    4. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    5. Constantin Bürgi & Tara M. Sinclair, 2017. "A nonparametric approach to identifying a subset of forecasters that outperforms the simple average," Empirical Economics, Springer, vol. 53(1), pages 101-115, August.
    6. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2014. "Improving Density Forecasts and Value-at-Risk Estimates by Combining Densities," Tinbergen Institute Discussion Papers 14-090/III, Tinbergen Institute.
    8. repec:kap:jgeosy:v:19:y:2017:i:4:d:10.1007_s10109-017-0259-9 is not listed on IDEAS
    9. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1181-6 is not listed on IDEAS
    10. repec:eee:jomega:v:78:y:2018:i:c:p:165-175 is not listed on IDEAS
    11. Espasa, Antoni & Senra, Eva, 2017. "22 Years of inflation assessment and forecasting experience at the bulletin of EU & US inflation and macroeconomic analysis," DES - Working Papers. Statistics and Econometrics. WS 24678, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Victor Lopez-Perez, 2016. "Macroeconomic Forecast Uncertainty In The Euro Area," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(1), pages 9-41, March.
    13. McAdam, Peter & Warne, Anders, 2018. "Euro area real-time density forecasting with financial or labor market frictions," Working Paper Series 2140, European Central Bank.
    14. Sami Oinonen & Maritta Paloviita, 2017. "How Informative are Aggregated Inflation Expectations? Evidence from the ECB Survey of Professional Forecasters," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(2), pages 139-163, November.
    15. repec:gam:jecnmx:v:5:y:2017:i:4:p:44-:d:114224 is not listed on IDEAS
    16. Víctor López-Pérez, 2017. "Do professional forecasters behave as if they believed in the New Keynesian Phillips Curve for the euro area?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 147-174, February.

    More about this item

    Keywords

    forecast combination; forecast evaluation; high-dimensional data; real-time data; shrinkage; Survey of Professional Forecasters;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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