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Evaluating Macroeconomic Forecasts:A Concise Review of Some Recent Developments

Author

Listed:
  • Philip Hans Franses

    (Econometric Institute Erasmus School of Economics Erasmus University Rotterdam)

  • Michael McAleer

    (Erasmus University Rotterdam,Tinbergen Institute,Kyoto University,Complutense University of Madrid)

  • Rianne Legerstee

    (Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, Tinbergen Institute The Netherlands)

Abstract

Macroeconomic forecasts are frequently produced, widely published, intensively 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 macroeconomic 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 econometric 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 intuition. It is shown that alternative tools are needed to compare and evaluate the forecasts 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.

Suggested Citation

  • Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2012. "Evaluating Macroeconomic Forecasts:A Concise Review of Some Recent Developments," KIER Working Papers 821, Kyoto University, Institute of Economic Research.
  • Handle: RePEc:kyo:wpaper:821
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    File URL: http://www.kier.kyoto-u.ac.jp/DP/DP821.pdf
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    References listed on IDEAS

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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 09:19:00

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    Cited by:

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    2. repec:aaa:journl:v:3:y:1999:i:1:p:87-100 is not listed on IDEAS
    3. Mihaela Simionescu (Bratu), 2014. "The Performance of Predictions Based on the Dobrescu Macromodel for the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 179-195, October.
    4. Claeys, Peter & Cimadomo, Jacopo & Poplawski Ribeiro, Marcos, 2014. "How do financial institutions forecast sovereign spreads?," Working Paper Series 1750, European Central Bank.
    5. Miquel Clar-Lopez & Jordi López-Tamayo & Raúl Ramos, 2014. "Unemployment forecasts, time varying coefficient models and the Okun’s law in Spanish regions," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 247-262.
    6. 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, vol. 35(2(44)), pages 128-138, December.
    7. Simionescu Mihaela, 2015. "Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?," Naše gospodarstvo/Our economy, Sciendo, vol. 61(3), pages 3-21, June.
    8. 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.
    9. 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.
    10. Mihaela Simionescu, 2015. "The Improvement of Unemployment Rate Predictions Accuracy," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(3), pages 274-286.

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    More about this item

    Keywords

    Macroeconomic forecasts; econometric models; human intuition; biased forecasts; forecast performance; forecast evaluation; forecast comparison.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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