Title: Nowcasting economic turning points with a simple machine-learning algorithm
Authors: Thomas Raffinot - Paris Dauphine (France) [presenting]
Abstract: To nowcast economic turning points, probabilistic indicators are created from a simple and transparent machine-learning algorithm known as Learning Vector Quantization (LVQ). The real-time ability of the indicators to quickly and accurately detect economic turning points in the United States and in the euro area is gauged. To assess the value of the indicators, profit maximization measures based on trading strategies are employed in addition to more standard criteria. When comparing predictive accuracy and profit measures, the model confidence set procedure is applied to avoid data snooping. A substantial improvement in profit measures over the benchmark is found: macroeconomists can get rich nowcasting economic turning points.