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Growth-at-Risk: Bayesian Approach

Author

Listed:
  • Milan Szabo

Abstract

The paper proposes a novel application of Bayesian quantile regression to forecast a full distribution of macroeconomic variables that can be linked to, for example, an official projection of the variable published by a central bank, or a forecast from a survey of professional forecasters. The approach is employed to estimate the popular Growth-at-Risk, which maps current financial and economic conditions to the distribution of future GDP growth, focusing mainly on downside risks. The results show that the linkage improves distribution forecasting and, thanks to the additional information obtained from the linkage, reduces overfitting and makes Growth-at-Risk models more operational for countries with short time series. Additional improvements in consistency around the official projection enhance the credibility of the results when communicated by the central bank. The method can also be used to derive asymmetric fan charts around the official projection not only for real GDP growth as examined in the paper, but also for unemployment or inflation.

Suggested Citation

  • Milan Szabo, 2020. "Growth-at-Risk: Bayesian Approach," Working Papers 2020/3, Czech National Bank, Research and Statistics Department.
    • Handle: RePEc:cnb:wpaper:2020/3
    as

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    References listed on IDEAS

    as
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    Keywords

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    JEL classification:

    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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