ECB: nowcasting using machine learning for economic regime prediction

Researchers from the European Central Bank propose a granular framework that makes use of advanced statistical methods to approximate developments in economy-wide expected corporate earnings. In particular, they evaluate the dynamic network structure of stock returns in the United States as a proxy for the transmission of shocks through the economy and identify node positions (firms) whose connectedness provides a signal for economic growth.

The nowcasting exercise, with both the in-sample and the out-of-sample consistent feature selection, highlights which firms are contemporaneously exposed to aggregate downturns and provides a more complete narrative than is usually provided by more aggregate data.

The two-state model for predicting periods of negative growth can remarkably well predict future states by using information derived from the node-positions of manufacturing, transportation and financial (particularly insurance) firms. The three-states model, which identifies high, low and negative growth, successfully predicts economic regimes by making use of information from the financial, insurance, and retail sectors.

The main findings can be summarised as follows. The baseline binary state model can predict upcoming recessions by using information from the node-positions of manufacturing, transportation and financial, particularly insurance firms. The ternary state model (featuring high, low growth, and recessions) successfully predicts economic regimes highlighting the role of information stemming from the financial, the insurance, and the retail sectors.

Looking at the economic system as a whole, researchers highlight that during an expansion adverse shocks to firms are mostly idiosyncratic, while during contractions shocks become more widespread resulting in higher connectedness in certain parts of the network. Measures of centrality efficiently summarise economy-wide developments and allow us to monitor the state of the economy in real time.

Read the full paper

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