arXiv 8 Jan 2024 · Econometrics
arXiv:2401.04050 · PDF · DOI · OpenAlex · Extracted main text
This article studies identification and estimation for the network vector autoregressive model with nonstationary regressors. In particular, network dependence is characterized by a nonstochastic adjacency matrix. The information set includes a stationary regressand and a node-specific vector of nonstationary regressors, both observed at the same equally spaced time frequencies. Our proposed econometric specification correponds to the NVAR model under time series nonstationarity which relies on the local-to-unity parametrization for capturing the unknown form of persistence of these node-specific regressors. Robust econometric estimation is achieved using an IVX-type estimator and the asymptotic theory analysis for the augmented vector of regressors is studied based on a double asymptotic regime where both the network size and the time dimension tend to infinity.
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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 | Zhu, X., Pan, R., Li, G., Liu, Y., Wang, H., et al (2017) Network vector autoregression | 1.000 | 8 | 3 | 100% |
| 2 | Armillotta, M. and Fokianos, K (2022) Nonlinear network autoregression | 0.843 | 3 | 3 | 100% |
| 3 | Phillips, P. C. B. and Magdalinos, T (2009) Econometric inference in the vicinity of unity | 0.811 | 4 | 2 | 100% |
| 4 | Magdalinos, T (2021) Least squares and ivx limit theory in systems of predictive regressions with garch innovations | 0.811 | 4 | 2 | 100% |
| 5 | Katsouris, C (2023) Statistical estimation for covariance structures with tail estimates using nodewise quantile predictive regression models self | 0.737 | 3 | 3 | 67% |
| 6 | Härdle, W. K., Wang, W., and Yu, L (2016) Tenet: Tail-event driven network risk | 0.737 | 3 | 2 | 100% |
| 7 | Kostakis, A., Magdalinos, T., and Stamatogiannis, M. P (2015) Robust econometric inference for stock return predictability | 0.737 | 3 | 2 | 100% |
| 8 | Phillips, P. C. B. and Magdalinos, T (2007) Limit theory for moderate deviations from a unit root | 0.737 | 3 | 2 | 100% |
| 9 | Agosto, A., Cavaliere, G., Kristensen, D., and Rahbek, A (2016) Modeling corporate defaults: Poisson autoregressions with exogenous covariates (parx) | 0.644 | 2 | 2 | 100% |
| 10 | Chen, C. Y.-H., Härdle, W. K., and Okhrin, Y (2019) Tail event driven networks of sifis | 0.644 | 2 | 2 | 100% |
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