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28-08-2023

Oil price shocks and macroeconomic dynamics: How important is the role of nonlinearity?

Authors: Inwook Hwang, Jaebeom Kim

Published in: Empirical Economics | Issue 3/2024

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Abstract

This study examines the state-dependent effects of crude oil price shocks on the US aggregate economy. Using a smooth transition vector autoregressive model, we assess the effects of normalized supply and demand shocks in the global oil market over the different phases of the business cycle. Our findings show that the link between oil prices and macroeconomic dynamics is nonlinear and that structural shocks in the oil market conditional on recessions have a greater and more persistent contractionary impact on macroeconomic variables including industrial production, employment, and consumer price inflation. Forecast error variance decomposition confirms that oil prices have much stronger stagflationary effects during recessions.

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Available only for authorised users

This index is available at Baumeister’s website (https://sites.google.com/site/cjsbaumeister/research).

The RAC series is available monthly starting in January 1974 and going back one more year following (Barsky and Kilian 2002), which allows us to include the first oil shock during October 1973 through March 1974.

They use the US GDP as the output. However, due to the data unavailability at monthly frequency, we use employment and industrial production as plausible substitutes to GDP.

The main goal is to describe the transition between different phases of the business cycle as accurate as possible in our model. For this purpose, we use 12 month moving average terms. The transition indicator with 12 months reflects well our goal than other terms over the observed frequencies of the US NBER-recessions. Details for this issue are available upon request.

The smoothness parameter \(\gamma \) is calibrated as defined by the “NBER based Recession Indicators for the United States from Peak through Trough” (https://fred.stlouisfed.org/series/USRECM). In the full sample, there are 78 recession indicators over 552 observations (that is, 78/552 \( \cong \) 14%). Then, “recessions” are defined as periods in which \(F (z_{t}) \ge \) 0.86 so that the probability of being in a recession is sufficiently high, and \(\gamma \) is calibrated in order that Pr(\(F (z_{t})\) \( \ge \) 0.86) \( \cong \) 14%, implying \(\gamma =\) 2.0.

Figure 1 plots the transition by our logistic function \(F (z_{t})\). \(F (z_{t})\) is highly correlated with NBER-recession indicator, and shows that high realization of \(F (z_{t})\) easily takes over threshold value \(F (z_{t}) \ge 0.86\) which is over each of the six NBER-recession episodes. The Pearson correlation coefficient between two data sets is \(r =0.45\) with associated p value = 0.00.

We use 20,000 draws for our baseline and robustness estimates and drop the first 80% draws to maintain the adequate acceptance rates of candidate draws. For details, refer to Auerbach and Gorodnichenko (2012).

The lag selection is consistent with previous literature (e.g., see (Rahman and Serletis 2010; Auerbach and Gorodnichenko 2012; Caggiano et al. 2014)).

For details, see Appendix 7.1.

For robustness, we consider different orderings of the second block, \(X_{2 t}\), and confirm that variations on the orderings are reasonable and markedly robust. The results are available upon request.

We appreciate the anonymous referee to point out this issue. To identify the structural shocks for the US-variable block, we also estimate four-variable models that contain only one of the US variables at a time and confirm that an absence of interaction between US macroeconomic variables somewhat weakens the contractionary effects of oil price shocks during recessionary periods. However, it does not substantially change the results from the baseline estimates (See Fig. 6). Further, we also run variations on the different orderings of the second block, \(X_{2 t}\) (See Sect. 5.3).

According to Kilian (2009), this residual is related with a precautionary demand for oil with high uncertainty because the more uncertain the global crude oil market, the higher the amount of precautionary demand of oil.

Following Kilian (2009), we normalize three structural shocks to represent to raise the real price of oil, implying that two demand-driven shocks are positive, while a supply shock is negative.

Global demand shocks have direct and indirect effects on the US economy through raising the price of oil. As shown in Kilian (2009), both effects on the aggregate economy are of opposite signs and the adverse effect of higher oil prices makes the aggregate demand shock contractionary with a delay. This delay is explained by two dynamic responses: (1) as global activity rises, the aggregate demand shock stimulates the US economic activity, and (2) the real price of oil increases and then output growth declines over time.

It reflects the fact that the Fed conducts monetary policy to influence the availability and cost of credit in the economy after economic shocks affect macroeconomic variables such as consumer price, labor market indicator, and output (e.g., see (Caggiano et al. 2014, 2017; Nodari 2014)).

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Title: Oil price shocks and macroeconomic dynamics: How important is the role of nonlinearity?
Authors: Inwook Hwang
Jaebeom Kim
Publication date: 28-08-2023
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 3/2024
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-023-02484-w

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