
doi: 10.2139/ssrn.3943743
handle: 10419/246186
This paper proposes an econometric framework for nowcasting the monetary policy stance and decisions of the European Central Bank (ECB) exploiting the ow of conventional and textual data that become available between two consecutive press conferences. Decompositions of the updated nowcasts into variables' marginal contribution are also provided to shed light on the main drivers of the ECB's reaction function at every point in time. In out-of-sample nowcasting experiments, the model provides an accurate tracking of the ECB monetary policy stance and decisions. The inclusion of textual variables contributes signifi- cantly to the gradual improvement of the model performance.
ddc:330, E37, Dynamic Factor Model, E47, E52, Natural Language Processing, Monetary Policy, Forecasting
ddc:330, E37, Dynamic Factor Model, E47, E52, Natural Language Processing, Monetary Policy, Forecasting
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