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How informative are the subjective density forecasts of macroeconomists?


  • Kenny, Geoff
  • Kostka, Thomas
  • Masera, Federico


In this paper, we propose a framework to evaluate the subjective density forecasts of macroeconomists using micro data from the euro area Survey of Professional Forecasters (SPF). A key aspect of our analysis is the evaluation of the entire predictive densities, including an evaluation of the impact of density features such as location, spread, skew and tail risk on density forecast performance. Overall, we find considerable heterogeneity in the performance of the surveyed densities at the individual level. Relative to a set of simple benchmarks, this performance is somewhat better for GDP growth than for inflation, although in the former case it diminishes substantially with the forecast horizon. In addition, we report evidence of some improvement in the relative performance of expert densities during the recent period of macroeconomic volatility. However, our analysis also reveals clear evidence of overconfidence or neglected risks in expert probability assessments, as reflected in frequent occurrences of events which are assigned a zero probability. Moreover, higher moment features of expert densities, such as skew or the degree of probability mass in their tails, are shown not to contribute significantly to improvements in individual density forecast performance. JEL Classification: C22, C53

Suggested Citation

  • Kenny, Geoff & Kostka, Thomas & Masera, Federico, 2012. "How informative are the subjective density forecasts of macroeconomists?," Working Paper Series 1446, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20121446

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    References listed on IDEAS

    1. Victor Zarnowitz & Louis A. Lambros, 1983. "Consensus and Uncertainty in Economic Prediction," NBER Working Papers 1171, National Bureau of Economic Research, Inc.
    2. Lahiri, Kajal & Teigland, Christie & Zaporowski, Mark, 1988. "Interest Rates and the Subjective Probability Distribution of Inflation Forecasts," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 20(2), pages 233-248, May.
    3. Robert Rich & Joseph Tracy, 2010. "The Relationships among Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 200-207, February.
    4. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    5. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
    6. repec:nsr:niesrd:320 is not listed on IDEAS
    7. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
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    Cited by:

    1. Oinonen, Sami & Paloviita, Maritta, 2014. "Analysis of aggregated inflation expectations based on the ECB SPF survey," Research Discussion Papers 29/2014, Bank of Finland.
    2. Maritta Paloviita and Matti Viren, 2012. "Are individual survey expectations internally consistent?," Discussion Papers 77, Aboa Centre for Economics.
    3. López Pérez, Víctor, 2015. "Does uncertainty affect participation in the European Central Bank's Survey of Professional Forecasters?," Working Paper Series 1807, European Central Bank.
    4. Rich, Robert W. & Tracy, Joseph, 2017. "The behavior of uncertainty and disagreement and their roles in economic prediction: a panel analysis," Staff Reports 808, Federal Reserve Bank of New York.
    5. Tomasz Łyziak & Maritta Paloviita, 2017. "Formation of inflation expectations in turbulent times. Recent evidence from the European Survey of Professional Forecasters," NBP Working Papers 261, Narodowy Bank Polski, Economic Research Department.
    6. Maritta Paloviita & Matti Viren, 2014. "Inflation and output growth uncertainty in individual survey expectations," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 69-81, February.
    7. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Density characteristics and density forecast performance: a panel analysis," Empirical Economics, Springer, vol. 48(3), pages 1203-1231, May.
    8. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Can Macroeconomists Forecast Risk? Event-Based Evidence from the Euro-Area SPF," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 1-46, December.
    9. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-017-1228-3 is not listed on IDEAS
    10. Paloviita, Maritta & Virén, Matti, 2014. "Analysis of forecast errors in micro-level survey data," Research Discussion Papers 8/2014, Bank of Finland.
    11. Joshua Abel & Robert Rich & Joseph Song & Joseph Tracy, 2016. "The Measurement and Behavior of Uncertainty: Evidence from the ECB Survey of Professional Forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 533-550, April.
    12. Łyziak, Tomasz & Paloviita, Maritta, 2017. "Formation of inflation expectations in turbulent times : Can ECB manage inflation expectations of professional forecasters?," Research Discussion Papers 13/2017, Bank of Finland.
    13. 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.
    14. Clements, Michael P, 2012. "Subjective and Ex Post Forecast Uncertainty : US Inflation and Output Growth," The Warwick Economics Research Paper Series (TWERPS) 995, University of Warwick, Department of Economics.

    More about this item


    forecast evaluation; neglected risks; real-time data; 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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