Financial nowcasts and their usefulness in macroeconomic forecasting
Edward Knotek () and
International Journal of Forecasting, 2019, vol. 35, issue 4, 1708-1724
Financial data often contain information that is helpful for macroeconomic forecasting, while multi-step forecast accuracy benefits from incorporating good nowcasts of macroeconomic variables. This paper considers the usefulness of financial nowcasts for making conditional forecasts of macroeconomic variables with quarterly Bayesian vector autoregressions (BVARs). When nowcasting quarterly financial variables’ values, we find that taking the average of the available daily data and a daily random walk forecast to complete the quarter typically outperforms other nowcasting approaches. Using real-time data, we find gains in out-of-sample forecast accuracy from the inclusion of financial nowcasts relative to unconditional forecasts, with further gains from the incorporation of nowcasts of macroeconomic variables. Conditional forecasts from quarterly BVARs augmented with financial nowcasts rival the forecast accuracy of mixed-frequency dynamic factor models and mixed-data sampling (MIDAS) models.
Keywords: Conditional forecasting; Nowcasting; Bayesian VARs; Mixed-frequency models; Real-time data (search for similar items in EconPapers)
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Working Paper: Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1708-1724
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