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Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors

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Abstract

We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to constant variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts. Our method can also be applied to other surveys like the Blue Chip Consensus, or the Federal Open Market Committee?s Summary of Economic Projections.

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  • Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2017. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," Working Papers 17-15R, Federal Reserve Bank of Cleveland.
  • Handle: RePEc:fip:fedcwq:171501
    DOI: 10.26509/frbc-wp-201715r
    Note: This paper is a revision of Working Paper 17-15 published in September of 2017.
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    Cited by:

    1. Kajal Lahiri & Huaming Peng & Xuguang Sheng, 2015. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," CESifo Working Paper Series 5468, CESifo.
    2. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
    3. Mirela Miescu, 2019. "Uncertainty shocks in emerging economies," Working Papers 277077821, Lancaster University Management School, Economics Department.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," Working Papers 202102R, Federal Reserve Bank of Cleveland, revised 09 Aug 2021.
    5. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    6. Barbara Rossi, 2019. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," Working Papers 1162, Barcelona Graduate School of Economics.
    7. Adams, Patrick A. & Adrian, Tobias & Boyarchenko, Nina & Giannone, Domenico, 2021. "Forecasting macroeconomic risks," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1173-1191.
    8. Galvao, Ana Beatriz & Mitchell, James, 2020. "Real-Time Perceptions of Historical GDP Data Uncertainty," EMF Research Papers 35, Economic Modelling and Forecasting Group.
    9. Galvao, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2020. "Does Judgment Improve Macroeconomic Density Forecasts?," EMF Research Papers 33, Economic Modelling and Forecasting Group.
    10. Weiqi Zhang & Huong Ha & Hui Ting Evelyn Gay, 2020. "Analysts’ forecasts between last consensus and earning announcement date," Journal of Financial Reporting and Accounting, Emerald Group Publishing, vol. 18(4), pages 779-793, November.
    11. Knüppel, Malte, 2018. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," International Journal of Forecasting, Elsevier, vol. 34(1), pages 105-116.
    12. Reifschneider, David & Tulip, Peter, 2019. "Gauging the uncertainty of the economic outlook using historical forecasting errors: The Federal Reserve’s approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1564-1582.

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    More about this item

    Keywords

    survey forecasts; fan charts; Stochastic volatility;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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