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Assessing U.S. Aggregate Fluctuations Across Time and Frequencies

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Abstract
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.

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  • Thomas A. Lubik & Christian Matthes & Fabio Verona, 2019. "Assessing U.S. Aggregate Fluctuations Across Time and Frequencies," Working Paper 19-6, Federal Reserve Bank of Richmond.
    • Handle: RePEc:fip:fedrwp:19-06
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    References listed on IDEAS

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    Cited by:

    1. Faria, Gonçalo & Verona, Fabio, 2023. "Forecast combination in the frequency domain," Bank of Finland Research Discussion Papers 1/2023, Bank of Finland.
    2. Crowley, Patrick M. & Hughes Hallett, Andrew, 2019. "The evolution of US and UK GDP components in the time-frequency domain: A continuous wavelet analysis," Bank of Finland Research Discussion Papers 23/2019, Bank of Finland.
    3. Crowley, Patrick M. & Hudgins, David, 2019. "U.S. Macroeconomic Policy Evaluation in an Open Economy Context using Wavelet Decomposed Optimal Control Methods," Bank of Finland Research Discussion Papers 11/2019, Bank of Finland.
    4. Hwang, Sun Ho & Kim, Yun Jung, 2021. "International output synchronization at different frequencies," Economic Modelling, Elsevier, vol. 104(C).
    5. Kilponen, Juha & Verona, Fabio, 2022. "Investment dynamics and forecast: Mind the frequency," Finance Research Letters, Elsevier, vol. 49(C).

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    More about this item

    Keywords

    Wavelets; bandpass filter; SVAR; sign restrictions; DSGE model;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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