Sectoral vs. Aggregate Shocks: A Structural Factor Analysis of Industrial Production

By Andrew T. Foerster, P. Sarte, Mark W. Watson. 2008

343 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f210e598e09737a15096135cf804e8312eed43d9

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In Economics and Econometrics

Citations in the corpus

Networks, Shocks, and Systemic Risk (2015, background): “…over input-output linkages can have potentially significant implications for macroeconomic volatility and economic growth.3 On the empirical side, Foerster, Sarte, and Watson (2011), Carvalho (2014), di Giovanni, Levchenko, and Méjean (2014), Acemoglu, Autor, Dorn, Hanson, and Price (2015a) and…”
From micro to macro via production networks (2014, background): “In another strand of the literature, Foerster et al. (2011) and Holly and Petrella (2012), explore econometrically the equilibrium structure of these models and conclude that input-output linkages serve as a powerful amplifier of otherwise independent shocks.”
Firm Volatility in Granular Networks (2013, background): “We build upon a rich literature exploring the role of input-output network linkages for aggregate fluctuations, exemplified by Long and Plosser (1987), Carvalho (2010), Foerster, Sarte, and Watson (2011), Acemoglu et al. (2012), and Carvalho and Gabaix (2013).”
The Great Diversification and its Undoing (2013, neutral): “Gabaix (2011) calls this the " granular " volatility.”
The Impact of Regional and Sectoral Productivity Changes on the U.S. Economy (2014, positive): “In contrast to regional changes, studying the effects of sectoral changes has a long tradition in the macroeconomics literature (see Long and Plosser, 1983, Horvath, 1998, Dupor, 1999, Foerster et al. 2011, and Acemoglu, et al. 2012, among many others).”
Supply Chain Disruptions: Evidence from the Great East Japan Earthquake* (2016, background): “While empirical studies, such as Foerster, Sarte, and Watson (2011), di Giovanni, Levchenko, and Méjean (2014), and Atalay (2017), investigate propagation of microeconomic shocks over input-output linkages, they invariably rely on strong identifying assumptions for backing out the shocks from data.”
Networks and the Macroeconomy: An Empirical Exploration (2016, neutral): “One branch of existing research has provided model- based quantitative evaluation of the importance of these interactions (e.g., Horvath 1998, 2000; Carvalho 2008; Foerster, Sarte, and Watson 2011).”
Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections (2015, background): “5 between common and idiosyncratic sources of fluctuations is often employed in international, regional and sectorial studies and represents a useful device to study macroeconomic implications of microeconomic behavior (see e.g. Kose, Otrok, and Whiteman, 2003; Foerster, Sarte, and Watson, 2011).”
Financial Frictions in Production Networks (2016, methodology): “Empirical evidence for these patterns has been found by Foerster et al. (2011).”
Input Specificity and the Propagation of Idiosyncratic Shocks in Production Networks (2016, methodology): “However, the empirical evidence on the importance of sector linkages for the aggregation of sector-specific shocks is mixed and depends on the level of aggregation (Horvath, 2000), the way linkages are modeled (Foerster et al., 2011), and the specification of the production function (Jones, 2011; At”
Production Networks: A Primer (2019, methodology): “23For comparison, Foerster et al. (2011) show that adopting the reduced-form approach of approximate factor models would lead one to conclude that two factors account for 87% of variability in aggregate industrial production between 1984 and 2007. imparted by Cobb-Douglas technologies.”