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A1116
Title: Financial nowcasts and their usefulness in macroeconomic forecasting Authors:  Edward Knotek - Federal Reserve Bank of Cleveland (United States) [presenting]
Saeed Zaman - Federal Reserve Bank of Cleveland (United States)
Abstract: The usefulness of financial nowcasts in making conditional forecasts of macroeconomic variables with quarterly Bayesian vector autoregressions (BVARs) is considered. For nowcasting quarterly financial variables' values, we find 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 incorporating nowcasts of macroeconomic variables. Conditional forecasts from quarterly BVARs augmented with financial nowcasts rival the forecast accuracy of dynamic factor and mixed-data sampling (MIDAS) models.