Assessing U.S. Aggregate Fluctuations Across Time and Frequencies
Christian Matthes (),
Thomas Lubik () and
Fabio Verona ()
No 19-6, Working Paper from Federal Reserve Bank of Richmond
We study the behavior of key macroeconomic variables in the time and frequency domain. For this purpose, we decompose U.S. time series into various frequency components. This allows us to identify a set of stylized facts: GDP growth is largely a high-frequency phenomenon whereby inflation and nominal interest rates are characterized largely by low-frequency components. In contrast, unemployment is a medium-term phenomenon. We use these decompositions jointly in a structural VAR where we identify monetary policy shocks using a sign restriction approach. We find that monetary policy shocks affect these key variables in a broadly similar manner across all frequency bands. Finally, we assess the ability of standard DSGE models to replicate these findings. While the models generally capture low-frequency movements via stochastic trends and business-cycle fluctuations through various frictions, they fail at capturing the medium-term cycle.
Keywords: Wavelets; bandpass filter; SVAR; sign restrictions; DSGE model (search for similar items in EconPapers)
JEL-codes: C32 C51 E32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge and nep-mac
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Working Paper: Assessing U.S. aggregate fluctuations across time and frequencies (2019)
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