Real Sectoral Spillovers

This paper studies changes in the transmission of common versus sectoral idiosyncratic shocks across different U.S. nonfarm business sectors during the Great Recession, and evaluates the cross-sectoral spillovers. Shocks are identified by dynamic factor methods. We find that the Great Recession is largely a time of heightened impact of common shocks— which accounts for 3/4 of aggregate volatility—and large spillovers of negative financerelated shocks. Moreover, in contrast with the earlier literature that failed to find a significant role of sectoral shocks (propagated through the input-output linkages across sectors) in driving variability in aggregate industry output, this study allows spillovers of shocks to operate through other mechanisms intertemporally. We find that prior to the recession the majority of aggregate fluctuations is explained by sector-specific shocks.

This paper studies changes in the transmission of common versus sectoral idiosyncraticshocks across different U.S. nonfarm business sectors during the Great Recession, andevaluates the cross-sectoral spillovers. Shocks are identified by dynamic factor methods. Wefind that the Great Recession is largely a time of heightened impact of common shocks-which accounts for 3/4 of aggregate volatility-and large spillovers of negative financerelatedshocks. Moreover, in contrast with the earlier literature that failed to find a significantrole of sectoral shocks (propagated through the input-output linkages across sectors) indriving variability in aggregate industry output, this study allows spillovers of shocks tooperate through other mechanisms intertemporally. We find that prior to the recession themajority of aggregate fluctuations is explained by sector-specific shocks.

This paper examines the role of sectoral spillovers in propagating sectoral shocks in the broader economy, both in the past and during the COVID-19 pandemic. In particular, we study how shocks that occur within a sector itself and spillovers from shocks to other sectors affect sectoral activity, for a large sample of countries from 1995 to 2014. We find that both supply and demand shocks—measured as changes in, respectively, productivity and government purchases at the sector level—have large spillover effects on sector-level gross value added and on a sector’s share of the economy. We then use these historical estimates, together with the network structure of global production, to quantify the spillovers from the economic shock associated with the pandemic. We find spillover effects to be sizeable, making up a significant fraction of the overall decline in activity in 2020.Our results have implications for the design of policies with a sectoral dimension.

This paper studies changes in the transmission of common versus sectoral idiosyncratic shocks across different U.S. nonfarm business sectors during the Great Recession, and evaluates the cross-sectoral spillovers. Shocks are identified by dynamic factor methods. We find that the Great Recession is largely a time of heightened impact of common shocks— which accounts for 3/4 of aggregate volatility—and large spillovers of negative financerelated shocks. Moreover, in contrast with the earlier literature that failed to find a significant role of sectoral shocks (propagated through the input-output linkages across sectors) in driving variability in aggregate industry output, this study allows spillovers of shocks to operate through other mechanisms intertemporally. We find that prior to the recession the majority of aggregate fluctuations is explained by sector-specific shocks.

This paper dives into the Fund’s historical coverage of cross-border spillovers in its surveillance. We use a state-of-the-art deep learning model to analyze the discussion of spillovers in all IMF Article IV staff reports between 2010 and 2019. We find that overall, while the discussion of spillovers decreased over time, it was pronounced in the staff reports of some systemically important economies and during periods of global spillover events. Spillover discussions were more prominent in staff reports covering advanced and emerging market economies, possibly reflecting their role as sources of global spillovers. The coverage of spillovers was higher in the context of the real, financial, and external sectors. Also, countries with larger economies, higher trade and capital account openess and lower inflation are more likely to discuss spillovers in their Article IV staff reports.

As a consequence of globalization, news, ideas and knowledge are moving quickly across national borders and generating international spillovers. So too, however, are economic and financial crises. Combining a variety of methods, concepts and interdisciplinary approaches, this book provides an in-depth examination of these structural changes and their impact. Assessing the implications of globalization for businesses and sectors, chapters focus on the interdependencies between different economic and political layers, and explore topics such as human capital, creativity, innovation, networks and collaboration.

This book presents a comprehensive view of the economic approach to the analysis of technical change.

Connections among different assets, asset classes, portfolios, and the stocks of individual institutions are critical in examining financial markets. Interest in financial markets implies interest in underlying macroeconomic fundamentals. In Financial and Macroeconomic Connectedness, Frank Diebold and Kamil Yilmaz propose a simple framework for defining, measuring, and monitoring connectedness, which is central to finance and macroeconomics. These measures of connectedness are theoretically rigorous yet empirically relevant. The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the 'connectedness table.' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authors also show that variance decompositions define weighted, directed networks, so that these proposed connectedness measures are intimately related to key measures of connectedness used in the network literature. After describing their methods in the first part of the book, the authors proceed to characterize daily return and volatility connectedness across major asset (stock, bond, foreign exchange and commodity) markets as well as the financial institutions within the U.S. and across countries since late 1990s. These specific measures of volatility connectedness show that stock markets played a critical role in spreading the volatility shocks from the U.S. to other countries. Furthermore, while the return connectedness across stock markets increased gradually over time the volatility connectedness measures were subject to significant jumps during major crisis events. This book examines not only financial connectedness, but also real fundamental connectedness. In particular, the authors show that global business cycle connectedness is economically significant and time-varying, that the U.S. has disproportionately high connectedness to others, and that pairwise country connectedness is inversely related to bilateral trade surpluses.