2006
DOI: 10.1016/j.red.2006.06.002
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How important is the intermediate input channel in explaining sectoral employment comovement over the business cycle?

Abstract: This paper studies a multisector dynamic stochastic general equilibrium model calibrated to the 2-digit SIC level intermediate input-use and capital-use tables to investigate the importance of the intermediate input channel in explaining the sectoral employment comovement over the business cycle. In general, the business cycle comovement in employment depends on preferences as well as technology with intersectoral linkages. With indivisible labor implying that consumers do not care about the variability of lei… Show more

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Cited by 34 publications
(25 citation statements)
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References 17 publications
(27 reference statements)
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“…With respect to aggregate variables, the multi-sector model reproduces important regularities of the U.S. data at the level that is similar to other multi-sector models (e.g. Horvath, 2000;Kim and Kim, 2006) and to the aggregated model with aggregate shocks. However, it outperforms the models of FS (2011) and ADD (2015) with respect to macroeconomic volatility.…”
Section: Business Cycle Properties: No Policysupporting
confidence: 65%
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“…With respect to aggregate variables, the multi-sector model reproduces important regularities of the U.S. data at the level that is similar to other multi-sector models (e.g. Horvath, 2000;Kim and Kim, 2006) and to the aggregated model with aggregate shocks. However, it outperforms the models of FS (2011) and ADD (2015) with respect to macroeconomic volatility.…”
Section: Business Cycle Properties: No Policysupporting
confidence: 65%
“…The prediction of low volatility of labor is shared with other multisector business cycle models (e.g. Kim and Kim, 2006). The model overpredicts capital and energy volatility in some sectors, which may be due to the absence of any frictions in capital and energy reallocation in our model.…”
Section: Business Cycle Properties: No Policysupporting
confidence: 50%
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“…Veldkamp and Wolfers (2007) nd that the average correlations of industry outputs and inputs with their aggregate counterparts all exceed 0.5. Murphy et al (1989), Shea (2002), and Kim and Kim (2006) also provide the estimation results of the correlations between industry variables and aggregate business cycle indicators. Using the factor analytic methods, Long and Plosser (1987), Forni and Reichlin (1998), Foerster et al (2008), and others suggest evidence that common factors account for a large fraction of sectoral uctuations.…”
Section: Introductionmentioning
confidence: 99%
“…Kim and Kim (2006) show that a similar mechanism generates widespread co-movement of economic activity (e.g. in employment) across sectors.…”
Section: Introductionmentioning
confidence: 88%