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How informative are the subjective density forecasts of macroeconomists?

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

Abstract

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

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 1446.

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Date of creation: Jul 2012
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Handle: RePEc:ecb:ecbwps:20121446

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Keywords: forecast evaluation; neglected risks; real-time data; Survey of Professional Forecasters;

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References

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  1. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
  2. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
  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. repec:nsr:niesrd:320 is not listed on IDEAS
  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. Victor Zarnowitz & Louis A. Lambros, 1983. "Consensus and Uncertainty in Economic Prediction," NBER Working Papers 1171, National Bureau of Economic Research, Inc.
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Cited by:
  1. Kenny, Geoff & Kostka, Thomas & Masera, Federico, 2013. "Can macroeconomists forecast risk? Event-based evidence from the euro area SPF," Working Paper Series 1540, European Central Bank.
  2. Robert Rich & Joseph Song & Joseph Tracy, 2012. "The measurement and behavior of uncertainty: evidence from the ECB Survey of Professional Forecasters," Staff Reports 588, Federal Reserve Bank of New York.
  3. Paloviita, Maritta & Viren, Matti, 2012. "Inflation and output growth uncertainty in individual survey expectations," Research Discussion Papers 37/2012, Bank of Finland.
  4. 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.
  5. Maritta Paloviita and Matti Viren, 2012. "Are individual survey expectations internally consistent?," Discussion Papers 77, Aboa Centre for Economics.
  6. Paloviita, Maritta & Viren, Matti, 2014. "Analysis of forecast errors in micro-level survey data," Research Discussion Papers 8/2014, Bank of Finland.

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