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Back testing fan charts of activity and inflation: the Chilean case

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  • Jorge Fornero
  • Andrés Gatty

Abstract

Any forecast has associated a measure of predictive uncertainty. The Central Bank of Chile (CBoC) communicates with fan charts the projections’ uncertainty of inflation and GDP growth in the Monetary Policy Report (MPR). This work aims at evaluating ex post their properties with empirical techniques. In general, we find that fan charts have been a relatively accurate in illustrating the true density generated by the conditional mean within forecasting horizons of up to one year. While inflation forecasts are unbiased, forecasts of GDP growth have been optimistic on average. The analysis of a recent sub-sample in which risks for GDP growth was made explicit, we graphically examine whether asymmetric fan charts are more accurate ex –post than symmetric fan charts. For these cases, the median projection seem to have provided a better guide than the mode.

Suggested Citation

  • Jorge Fornero & Andrés Gatty, 2020. "Back testing fan charts of activity and inflation: the Chilean case," Working Papers Central Bank of Chile 881, Central Bank of Chile.
    • Handle: RePEc:chb:bcchwp:881
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    References listed on IDEAS

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    1. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    2. Jeffrey Frankel, 2011. "Over-optimism in forecasts by official budget agencies and its implications," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 27(4), pages 536-562.
    3. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    4. Michael P. Clements, 2004. "Evaluating the Bank of England Density Forecasts of Inflation," Economic Journal, Royal Economic Society, vol. 114(498), pages 844-866, October.
    5. Jorge Fornero & Markus Kirchner & Andrés Yany, 2015. "Terms of Trade Shocks and Investment in Commodity-Exporting Economies," Central Banking, Analysis, and Economic Policies Book Series, in: Rodrigo Caputo & Roberto Chang (ed.),Commodity Prices and Macroeconomic Policy, edition 1, volume 22, chapter 5, pages 135-193, Central Bank of Chile.
    6. Francisco Arroyo Marioli & Francisco Bullano & Jorge Fornero & Roberto Zúñiga, 2020. "Semi-Structural Forecasting Model," Working Papers Central Bank of Chile 866, Central Bank of Chile.
    7. Juan Pablo Medina & Claudio Soto, 2007. "The Chilean Business Cycles Through the Lens of a Stochastic General Equilibrium Model," Working Papers Central Bank of Chile 457, Central Bank of Chile.
    8. Wallis, Kenneth F., 2003. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," International Journal of Forecasting, Elsevier, vol. 19(2), pages 165-175.
    9. Kenneth F. Wallis, 2004. "An Assessment of Bank of England and National Institute Inflation Forecast Uncertainties," National Institute Economic Review, National Institute of Economic and Social Research, vol. 189(1), pages 64-71, July.
    10. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    11. Tilmann Gneiting & Roopesh Ranjan, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 411-422, July.
    12. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Money Affairs, CEMLA, vol. 0(1), pages 37-73, January-J.
    13. David Turner & Thomas Chalaux & Hermes Morgavi, 2018. "Fan charts around GDP projections based on probit models of downturn risk," OECD Economics Department Working Papers 1521, OECD Publishing.
    14. Yi‐Ting Chen, 2011. "Moment tests for density forecast evaluation in the presence of parameter estimation uncertainty," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(4), pages 409-450, July.
    15. Gneiting, Tilmann & Ranjan, Roopesh, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 411-422.
    16. Klaus-Peter Hellwig, 2018. "Overfitting in Judgment-based Economic Forecasts: The Case of IMF Growth Projections," IMF Working Papers 2018/260, International Monetary Fund.
    17. Dowd, Kevin, 2007. "Too good to be true? The (In)credibility of the UK inflation fan charts," Journal of Macroeconomics, Elsevier, vol. 29(1), pages 91-102, March.
    18. James Mitchell & Kenneth F. Wallis, 2011. "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 1023-1040, September.
    19. Elías Albagli I. & Jorge A. Fornero & Miguel A. Fuentes D. & Roberto Zúñiga V., 2019. "On the effects of confidence and uncertainty on aggregate demand: evidence from Chile," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(3), pages 008-033, December.
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