Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/229108
Authors: 
Azqueta-Gavaldon, Andres
Hirschbühl, Dominik
Onorante, Luca
Saiz, Lorena
Year of Publication: 
2020
Series/Report no.: 
ECB Working Paper No. 2494
Abstract: 
We propose a granular framework that makes use of advanced statistical methods to approximate developments in economy-wide expected corporate earnings. In particular, we 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.
Subjects: 
real-time
turning point prediction
Granger-causality networks
early warningsignal
JEL: 
C45
C51
D85
E32
N1
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-4411-3
Document Type: 
Working Paper

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