order to compare the global GDP factor with other measures of global economic activity, Figure 3 (bottom) plots the global GDP factor alongside an index of world industrial production and the Kilian’s index (see Kilian, 2009; Hamilton, 2019). The global GDP factor and the world industrial production are highly correlated, with correlation 0.85, whereas the correlation between the global GDP factor and the Kilian index is 0.14.<sup>20</sup> The histogram in Figure 13, Appendix VII.1, provides a closer look at the importance of the global GDP factor in accounting for fluctuations in economic activity across countries. The figure shows the distribution of the amount of variance in country GDP growth rates accounted for by the global GDP factor. The results indicate that in the majority of countries, the global GDP factor explains a significant fraction of GDP growth volatility; it accounts for 21.6 percent of output variability across countries on average.<sup>21</sup> The contribution of the global GDP factor to country GDP growth rates is highest for oil importing advanced economies (32.9 percent) followed by oil exporters (21.8 percent), and its contribution to the fluctuations in output growth rates of oil importing emerging markets and developing countries is 12.4 percent.

# **IV.3.2 Global Economic Activity and U.S. Energy Market Shocks**

This section provides the results from the FAVAR Model 5 described in Section IV.2.2. I first discuss the impulse responses of the endogenous variables to U.S. energy efficiency and U.S. oil supply shocks. These results compare the average effects of the two shocks over the period 1980– 2019. I then present the historical decompositions to assess the impact of U.S. energy efficiency and oil supply shocks in the evolution of global economic activity during 2010–2019, a period that overlaps with the shale revolution.

**_Impulse Responses._** Figure 4 presents the IRFs of endogenous variables to U.S. energy market shocks. The left and right columns plot impulse responses for non-U.S. oil production, the global GDP factor, the real price of oil, U.S. oil production, and U.S. oil consumption to a one standard deviation increase in U.S. oil supply and U.S. energy efficiency, respectively.<sup>22</sup> The results are based on the combined restrictions discussed in Section IV.2.2. The results for the global GDP factor, oil

> 20The Kilian index is a popular measure of worldwide economic activity and it is used in many studies (see, for example, Baumeister & Kilian, 2012; Kilian & Murphy, 2014; Lütkepohl & Netšunajev, 2014; Anzuini, Pagano, & Pisani, 2015; Antolín-Díaz & Rubio-Ramírez, 2018). The Kilian’s index is constructed using cost of international shipping in commodity markets. The shipbuilding cycle moves more slowly relative to the business cycle due to the time involved in scrapping and launching ships (see Kilian & Zhou, 2018). Thus, the Kilian index captures longer swings in economic activity, which are important for accounting for cycles in commodity prices. However, because the objective of the analysis is to assess the impact of U.S. energy shocks on short-run fluctuations, I use the estimated global GDP factor in the structural VAR model instead of the Kilian index. Moreover, I multiply the estimated global GDP factor with the average factor loading in the subsequent analysis. The resulting measure is a moderated world business cycle relative to the world industrial production index. Consequently, the estimates of the impact of U.S. energy shocks on global economic activity will be more conservative.

> 21The variation in each observed variable can be decomposed as _var_ ( _yi,t_ ) = ( _βiGlobal_ )<sup>2</sup> _var_ ( _ft_<sup>_Global_</sup> )+ _var_ ( _εi,t_ ). Thus, the share of variance due to the global factor is given as ( _βi_<sup>_Global_</sup> _var_ )<sup>2</sup> _var_ ( _yi,t_ ( _f_ ) _<u>t</u>_<sup>_Global_</sup> ) _._

> 22Note that IRFs are reported without parameter estimation uncertainty. The median, 16 _th_ , and 84 _th_ percentiles impulse responses are reported from the admissible models. The results, thus, summarize the set of admissible models and focus on identification uncertainty (i.e., value of **B0** ), which is due to inequality restrictions.

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