arXiv 24 Nov 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2211.13610 · PDF · DOI · OpenAlex · Extracted main text
Many environments in economics involve units linked by bilateral ties. I develop an econometric framework that rationalizes the dynamics of cross-sectional variables as the innovation transmission along fixed bilateral links and that can accommodate rich patterns of how network effects of higher order accumulate over time. The proposed Network-VAR (NVAR) can be used to estimate dynamic network effects, with the network given or inferred from dynamic cross-correlations in the data. In the latter case, it also offers a dimensionality-reduction technique for modeling high-dimensional (cross-sectional) processes, owing to networks' ability to summarize complex relations among variables (units) by relatively few bilateral links. In a first application, I show that sectoral output growth in an RBC economy with lagged input-output conversion follows an NVAR. I characterize impulse-responses to TFP shocks in this environment, and I estimate that the lagged transmission of productivity shocks along supply chains can account for a third of the persistence in aggregate output growth. The remainder is due to persistence in the aggregate TFP process, leaving a negligible role for persistence in sectoral TFP. In a second application, I forecast macroeconomic aggregates across OECD countries by assuming and estimating a network that underlies the dynamics. In line with an equivalence result I provide, this reduces out-of-sample mean squared errors relative to a dynamic factor model. The reductions range from -12% for quarterly real GDP growth to -68% for monthly CPI inflation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Long, Jr., J. B. and C. I. Plosser (1983) Real Business Cycles | 1.000 | 12 | 3 | 100% |
| 2 | Carter, C. K. and R. Kohn (1994) On Gibbs Sampling for State Space Models | 1.000 | 5 | 3 | 100% |
| 3 | Carvalho, V. M. and A. Tahbaz-Salehi (2019) Production Networks: A Primer | 1.000 | 5 | 3 | 100% |
| 4 | Zhu, X., R. Pan, G. Li, Y. Liu, and H. Wang (2017) Network vector autoregression | 0.874 | 5 | 2 | 100% |
| 5 | Acemoglu, D., V. M. Carvalho, A. Ozdaglar, and A. Tahbaz-Salehi (2012) The Network Origins of Aggregate Fluctuations | 0.811 | 4 | 2 | 100% |
| 6 | Golub, B. and M. O. Jackson (2010) Naive Learning in Social Networks and the Wisdom of Crowds | 0.811 | 4 | 2 | 100% |
| 7 | Boivin, J. and S. Ng (2006) Are more data always better for factor analysis? | 0.737 | 3 | 2 | 100% |
| 8 | Carvalho, V. M (2010) Aggregate Fluctuations and The Network Structure of Intersectoral Trade | 0.737 | 3 | 2 | 100% |
| 9 | De Graeve, F. and J. D. Schneider (2023) Identifying sectoral shocks and their role in business cycles | 0.737 | 3 | 2 | 100% |
| 10 | Foerster, A. T., P.-D. G. Sarte, and M. W. Watson (2011) Sectoral versus Aggregate Shocks: A Structural Factor Analysis of Industrial Production | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 86 scored citations.