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Abstract
This study investigates the direct and intersectoral network effects of idiosyncratic TFP shocks on sectors’ growth in the context of US manufacturing industries. To deal with the potential endogeneity of TFP, we propose a novel set of instruments for contemporaneous regressors. These instruments are technology shocks identified via sign restriction from sectoral SVAR models. Using US input-output tables and industry-level data, we quantify direct and network-based effects of the shocks. Our results show that idiosyncratic technology shocks propagate mostly downstream the network. In addition, we capture strong contemporaneous direct effects of the shocks.
JEL Codes: C36, C67, D24, E32.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
Direct and network effects of idiosyncratic TFP shocks
Intersectoral network-based channel of aggregate TFP shocks
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Abstract
This study investigates the role of intersectoral networks in the transmission of aggregate technology shocks to sectors’ growth. First, we develop a theoretical model to obtain insights into the propagation of shocks through input-output linkages, which suggests that the network effect arises via sectoral downstream linkages. We then quantitatively assess this theoretical implication with US manufacturing industries, where the aggregate technology shocks are derived from a dynamic factor model. We find that aggregate technology shocks lead to an increase in the output growth of the sector, both directly and indirectly via its intersectoral linkages. More interestingly, we document a crucial role of the intersectoral network channel, which contributes about 50 percent of the total effect. In addition, the network-based effect comes mostly from downstream linkages of sectors, which is broadly consistent with theory.
JEL Codes: E32, C67, C33, L16, D24.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
Sectoral production and diffusion index forecasts for output in Lithuania
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Abstract
In this paper, we develop and describe quarterly data on disaggregated sectors in Lithuania which covers the period 1998-2018. The data is useful for empirical studies requiring panels with a large number of time observations and a large number of cross-sectional units. We follow the NACE2 level of disaggregation in developing our data, thus allowing us to combine the data with world input-output tables which we in turn use to identify the hubs and the main importing and exporting sectors within the economy. The data is then used for forecasting the growth rate of GDP. There is a substantial increase in the degree of covariation among sectoral production growth rates, which is observed using a split sample around 2008. When we apply factor methods, we find that this strong covariation can be explained by a few factors which are closely correlated to the growth of the retail and wholesale sectors. For GDP growth, the forecasts we consider are the diffusion index forecasts produced using a few indexes that summarize sectoral data, and the forecasts produced using the production growth of selected hubs and importing and exporting sectors. We find that the diffusion indexes and the production growth of sectors that make heavy use of imported inputs in their production have interesting forecasting power for the growth rate of GDP in the 2006-2011 and 2012-2018 periods.
JEL Codes: E27, E37, C3, C67.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.