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Evaluating Macroeconomic Forecasts: A Concise Review of Some Recent Developments

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  • Philip Hans Franses
  • Michael McAleer

    ()
    (University of Canterbury)

  • Rianne Legerstee

Abstract

Macroeconomic forecasts are frequently produced, widely published, inten¬sively discussed and comprehensively used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyse some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macro¬economic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC) and the ECB, are typically based on econometric model forecasts jointly with human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes non-standard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econo¬metric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model and intuition; and (iii) the two forecasts are generated from two distinct (but unknown) combinations of different models and intu¬ition. It is shown that alternative tools are needed to compare and evaluate the fore-casts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the (econometric) Staff of the Federal Reserve Board and the FOMC on inflation, unemployment and real GDP growth. It is shown that the FOMC does not forecast significantly better than the Staff, and that the intuition of the FOMC does not add significantly in forecasting the actual values of the economic fundamentals. This would seem to belie the purported expertise of the FOMC.

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Bibliographic Info

Paper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 12/12.

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Length: 25 pages
Date of creation: 08 Jun 2012
Date of revision:
Handle: RePEc:cbt:econwp:12/12

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Keywords: Macroeconomic forecasts; econometric models; human intuition; biased forecasts; forecast performance; forecast evaluation; forecast comparison;

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References

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  1. Franses, Philip Hans & Legerstee, Rianne, 2009. "Properties of expert adjustments on model-based SKU-level forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 25(1), pages 35-47.
  2. Chang, Chia-Lin & Franses, Philip Hans & McAleer, Michael, 2011. "How accurate are government forecasts of economic fundamentals? The case of Taiwan," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(4), pages 1066-1075, October.
  3. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
  4. McAleer, Michael, 1992. "Efficient Estimation: The Rao-Zyskind Condition, Kruskal's Theorem and Ordinary Least Squares," The Economic Record, The Economic Society of Australia, vol. 68(200), pages 65-72, March.
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  6. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
  7. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 466-481.
  8. Franses, Ph.H.B.F. & McAleer, M.J. & Legerstee, R., 2008. "Expert opinion versus expertise in forecasting," Econometric Institute Research Papers EI 2008-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  9. Todd E. Clark & Michael W. McCracken, 1999. "Tests of equal forecast accuracy and encompassing for nested models," Research Working Paper, Federal Reserve Bank of Kansas City 99-11, Federal Reserve Bank of Kansas City.
  10. Fiebig, Denzil G. & McAleer, Michael & Bartels, Robert, 1992. "Properties of ordinary least squares estimators in regression models with nonspherical disturbances," Journal of Econometrics, Elsevier, Elsevier, vol. 54(1-3), pages 321-334.
  11. Eroglu, Cuneyt & Croxton, Keely L., 2010. "Biases in judgmental adjustments of statistical forecasts: The role of individual differences," International Journal of Forecasting, Elsevier, Elsevier, vol. 26(1), pages 116-133, January.
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  13. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, Econometric Society, vol. 64(5), pages 1067-84, September.
  14. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, Elsevier, vol. 25(1), pages 3-23.
  15. Chong, Yock Y & Hendry, David F, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 53(4), pages 671-90, August.
  16. Roy Batchelor, 2007. "Forecaster Behaviour and Bias in Macroeconomic Forecasts," Ifo Working Paper Series Ifo Working Paper No. 39, Ifo Institute for Economic Research at the University of Munich.
  17. Franses, Philip Hans & Kranendonk, Henk C. & Lanser, Debby, 2011. "One model and various experts: Evaluating Dutch macroeconomic forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 482-495.
  18. Batchelor, Roy, 2007. "Bias in macroeconomic forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 23(2), pages 189-203.
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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. What I Learned Last Week
    by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-10-13 04:19:00
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Cited by:
  1. Mihaela BRATU (SIMIONESCU), 2012. "A Strategy To Improve The Gdp Index Forcasts In Romania Using Moving Average Models Of Historical Errors Of The Dobrescu Macromodel," Romanian Journal of Economics, Institute of National Economy, Institute of National Economy, vol. 35(2(44)), pages 128-138, December.
  2. Bratu Mihaela, 2013. "An Evaluation Of Usa Unemployment Rate Forecasts In Terms Of Accuracy And Bias. Empirical Methods To Improve The Forecasts Accuracy," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 170-180, February.
  3. Mihaela Bratu, 2012. "A Strategy to Improve the Survey of Professional Forecasters (SPF) Predictions Using Bias-Corrected-Accelerated (BCA) Bootstrap Forecast Intervals," International Journal of Synergy and Research, ToKnowPress, vol. 1(2), pages 45-59.

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