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Business cycles in the USA: the role of monetary policy and oil shocks

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Abstract

This paper examines the relative significance of oil supply, oil demand, and monetary policy shocks in explaining US macroeconomic variations. We analyze impulse response functions and variance decomposition to assess the relative importance of these shocks. Using a Bayesian structural VAR framework and the penalty function approach, we identify the shocks of interest. We find that oil supply shocks explain less than 3% of the variation in output, but have a relatively larger impact on inflation, accounting for around 13% of the inflation variation. Oil demand shocks explain 3% of output variation, but contribute significantly to inflation variation (around 16%). In contrast, monetary policy shocks have a greater influence on output, explaining approximately 13% of the observed variation. Monetary policy shocks are also the most influential source of inflation variation, contributing over 24% to the overall variation. Based on historical variance decomposition, we find that the recent inflation surge is attributable to both monetary expansion and oil supply factors. Overall, the study highlights the dominance of monetary policy shocks in explaining US macroeconomic fluctuations, with oil supply and demand shocks playing secondary roles.

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Data will be available upon request.

Notes

  1. Based on the authors’ calculations using data from the Federal Reserve Economic Database (FRED).

  2. We use consumption in nominal terms and although nominal consumption might be highly correlated with the inflation covariate, we show in robustness tests that the results are not affected by excluding nominal consumption (growth).

  3. In Sect. 5, we estimate the model with the growth rate of real GDP as well as an alternative measure of the output gap.

  4. See https://sites.google.com/site/cjsbaumeister/datasets?authuser=0 for these series.

  5. See Uhlig (2005), Mountford and Uhlig (2009), Caldara et al. (2016), Dery and Serletis (2023) for further information on the penalty function approach.

  6. Examples of papers with this characterization: (Uhlig 2005; Mountford and Uhlig 2009; Caldara et al. 2016; Dery and Serletis 2023)

  7. Since for any orthonormal matrix \({\varvec{S}}, \widetilde{{ {\varvec{A}} }}^{-1}= {\varvec{T}} {\varvec{S}}\) is also a decomposition that satisfies \([\widetilde{{ {\varvec{A}} }}\widetilde{{ {\varvec{A}} }}^{^{\prime }}]^{\prime }=\varvec{\Omega }\), where \({\varvec{T}}\) is a Cholesky factorization of \(\varvec{\Omega }\).

  8. This refers to an issue encountered with pure sign restrictions, where multiple models with identified parameters can rationalize the data. As a result, while pure sign restrictions achieve parameter identification, they may not achieve model identification. Thus, summarizing the responses using for instance the median response and conventional error bands represent the range of response distributions across these different models. See Fry and Pagan (2011).

  9. Note that using two quarters yields similar results but we impose the restrictions for one quarter here to reflect our desire to have less restrictive assumptions.

  10. The forecast error variance decomposition of the interest rate, exchange rate, consumption, and money supply to the three shocks is shown in Appendix Figs. 15, 16, and 17, respectively, for the oil supply, oil demand, and monetary policy shocks.

  11. We re-estimated the baseline model using the shadow federal funds rate for time periods in which the effective federal funds rate had reached the zero lower bound. For conciseness, we do not report those results as they are identical to results already presented in the figures.

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Authors and Affiliations

  1. Department of Economics and International Business, Sam Houston State University, Huntsville, TX, 77341, USA

    Cosmas Dery

  2. Department of Economics, University of Calgary, Calgary, AB, T2N 1N4, Canada

    Apostolos Serletis

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  1. Cosmas Dery
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  2. Apostolos Serletis
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Correspondence to Apostolos Serletis.

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We would like to thank two referees for comments that greatly improved the paper. Cosmas Dery thanks the College of Business Administration of Sam Houston State University for a Summer Research Grant that provided support for this research.

Appendix

Appendix

Fig. 16
figure 16

Variance decomposition of variables to oil demand shock. Note: The solid black line is the posterior median response, while the blue dashed lines are the corresponding Bayesian 68% confidence intervals. (Color figure online)

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Fig. 17
figure 17

Variance decomposition of variables to monetary policy shocks. Note: The solid black line is the posterior median response, while the blue dashed lines are the corresponding Bayesian 68% confidence intervals. (Color figure online)

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See Figs. 12, 13, 14, 15, 16, 17 and

Table 1 Definitions of hyperparameters
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Table 2 CMA-ES estimates
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Table 3 Variables in each model and order of shocks
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Dery, C., Serletis, A. Business cycles in the USA: the role of monetary policy and oil shocks. Empir Econ 67, 1–30 (2024). https://doi.org/10.1007/s00181-024-02556-5

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