FNETS: Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

References

• Adamek, R., Smeekes, S., and Wilms, I. (2023), “Lasso Inference for High-Dimensional Time Series,” Journal of Economics, 235, 1114–1143. DOI: 10.1016/j.jeconom.2022.08.008.
   Google Scholar
• Ahelegbey, D. F., Billio, M., and Casarin, R. (2016), “Bayesian Graphical Models for Structural Vector Autoregressive Processes,” Journal of Applied Economics, 31, 357–386. DOI: 10.1002/jae.2443.
   Web of Science ®Google Scholar
• Ahn, S. C., and Horenstein, A. R. (2013), “Eigenvalue Ratio Test for the Number of Factors,” Econometrica, 81, 1203–1227.
   Web of Science ®Google Scholar
• Anderson, B. D., and Deistler, M. (2008), “Generalized Linear Dynamic Factor Models–A Structure Theory,” in 47th IEEE Conference on Decision and Control, pp. 1980–1985.
   Google Scholar
• Bai, J. (2003), “Inferential Theory for Factor Models of Large Dimensions,” Econometrica, 71, 135–171. DOI: 10.1111/1468-0262.00392.
   Web of Science ®Google Scholar
• Bai, J., and Ng, S. (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221. DOI: 10.1111/1468-0262.00273.
   Web of Science ®Google Scholar
• Bańbura, M., Giannone, D., and Reichlin, L. (2010), “Large Bayesian Vector Auto Regressions,” Journal of Applied Economics, 25, 71–92. DOI: 10.1002/jae.1137.
   Web of Science ®Google Scholar
• Barigozzi, M., and Brownlees, C. (2019), “NETS: Network Estimation for Time Series,” Journal of Applied Economics, 34, 347–364. DOI: 10.1002/jae.2676.
   Web of Science ®Google Scholar
• Barigozzi, M., Cho, H., and Owens, D. (2023), fnets: Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series, R package version 0.1.5. DOI: 10.1080/07350015.2023.2257270.
   Google Scholar
• Barigozzi, M., and Hallin, M. (2017), “Generalized Dynamic Factor Models and Volatilities: Estimation and Forecasting,” Journal of Economics, 201, 307–321. DOI: 10.1016/j.jeconom.2017.08.010.
   Google Scholar
• Basu, S., Li, X., and Michailidis, G. (2019), “Low Rank and Structured Modeling of High-Dimensional Vector Autoregressions,” IEEE Transactions on Signal Processing, 67, 1207–1222. DOI: 10.1109/TSP.2018.2887401.
   Web of Science ®Google Scholar
• Basu, S., and Michailidis, G. (2015), “Regularized Estimation in Sparse High-Dimensional Time Series Models,” The Annals of Statistics, 43, 1535–1567. DOI: 10.1214/15-AOS1315.
   Web of Science ®Google Scholar
• Bickel, P. J., Ritov, Y., and Tsybakov, A. B. (2009), “Simultaneous Analysis of Lasso and Dantzig Selector,” The Annals of Statistics, 37, 1705–1732. DOI: 10.1214/08-AOS620.
   Web of Science ®Google Scholar
• Billio, M., Getmansky, M., Lo, A. W., and Pelizzon, L. (2012), “Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors,” Journal of Financial Economics, 104, 535–559. DOI: 10.1016/j.jfineco.2011.12.010.
   Web of Science ®Google Scholar
• Brillinger, D. R. (1981), Time Series: Data Analysis and Theory, Philadelphia, PA: SIAM.
   Google Scholar
• Brownlees, C., and Gallo, G. (2010), “Comparison of Volatility Measures: A Risk Management Perspective,” Journal of Financial Economics, 8, 29–56. DOI: 10.1093/jjfinec/nbp009.
   Web of Science ®Google Scholar
• Cai, T., Liu, W., and Luo, X. (2011), “A Constrained l1 Minimization Approach to Sparse Precision Matrix Estimation,” Journal of the American Statistical Association, 106, 594–607.
   Web of Science ®Google Scholar
• Candès, E. and Tao, T. (2007), “The Dantzig Selector: Statistical Estimation when p is much larger than n,” The Annals of Statistics, 35, 2313–2351. DOI: 10.1214/009053607000000532.
   Web of Science ®Google Scholar
• Cule, E., Vineis, P., and De Iorio, M. (2011), “Significance Testing in Ridge Regression for Genetic Data,” BMC Bioinformatics, 12, 1–15. DOI: 10.1186/1471-2105-12-372.
   PubMed Web of Science ®Google Scholar
• Dahlhaus, R. (2000), “Graphical Interaction Models for Multivariate Time Series,” Metrika, 51, 157–172. DOI: 10.1007/s001840000055.
   Web of Science ®Google Scholar
• Diebold, F. X., and Yilmaz, K. (2014), “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms,” Journal of Economics, 182, 119–134. DOI: 10.1016/j.jeconom.2014.04.012.
   Web of Science ®Google Scholar
• Eichler, M. (2007), “Granger Causality and Path Diagrams for Multivariate Time Series,” Journal of Economics, 137, 334–353. DOI: 10.1016/j.jeconom.2005.06.032.
   Google Scholar
• Fan, J., Ke, Y., and Wang, K. (2020), “Factor-Adjusted Regularized Model Selection,” Journal of Economics, 216, 71–85. DOI: 10.1016/j.jeconom.2020.01.006.
   Web of Science ®Google Scholar
• Fan, J., Liao, Y., and Mincheva, M. (2013), “Large Covariance Estimation by Thresholding Principal Orthogonal Complements,” Journal of the Royal Statistical Society, Series B, 75, 603–680. DOI: 10.1111/rssb.12016.
   Google Scholar
• Fan, J., Lou, Z., and Yu, M. (2023), “Are Latent Factor Regression and Sparse Regression Adequate?,” Journal of the American Statistical Association (in press). DOI: 10.1080/01621459.2023.2169700.
   Web of Science ®Google Scholar
• Fan, J., Masini, R., and Medeiros, M. C. (2021), “Bridging Factor and Sparse Models,” arXiv preprint arXiv:2102.11341 .
   Google Scholar
• Forni, M., Hallin, M., Lippi, M., and Reichlin, L. (2000), “The Generalized Dynamic Factor Model: Identification and Estimation,” The Review of Economics and Statistics, 82, 540–554. DOI: 10.1162/003465300559037.
   Web of Science ®Google Scholar
• ———(2005), “The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting,” Journal of the American Statistical Association, 100, 830–840.
   Web of Science ®Google Scholar
• Forni, M., Hallin, M., Lippi, M., and Zaffaroni, P. (2017), “Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis,” Journal of Economics, 199, 74–92. DOI: 10.1016/j.jeconom.2017.04.002.
   Google Scholar
• Forni, M., and Lippi, M. (2001), “The Generalized Dynamic Factor Model: Representation Theory,” Economic Theory, 17, 1113–1141. DOI: 10.1017/S0266466601176048.
   Web of Science ®Google Scholar
• Freyaldenhoven, S. (2021), “Factor Models with Local Factors–Determining the Number of Relevant Factors,” Journal of Economics, 229, 80–102. DOI: 10.1016/j.jeconom.2021.04.006.
   Google Scholar
• Giannone, D., Lenza, M., and Primiceri, G. E. (2021), “Economic Predictions With Big Data: The Illusion of Sparsity,” Econometrica, 89, 2409–2437. DOI: 10.3982/ECTA17842.
   Web of Science ®Google Scholar
• Guðmundsson, G. S., and Brownlees, C. (2021), “Detecting Groups in Large Vector Autoregressions,” Journal of Economics, 225, 2–26. DOI: 10.1016/j.jeconom.2021.03.012.
   Google Scholar
• Hallin, M., and Liška, R. (2007), “Determining the Number of Factors in the General Dynamic Factor Model,” Journal of the American Statistical Association, 102, 603–617. DOI: 10.1198/016214506000001275.
   Web of Science ®Google Scholar
• Han, F., Lu, H., and Liu, H. (2015), “A Direct Estimation of High Dimensional Stationary Vector Autoregressions,” Journal of Machine Learning Research, 16, 3115–3150.
   Web of Science ®Google Scholar
• Hsu, N.-J., Hung, H.-L., and Chang, Y.-M. (2008), “Subset Selection for Vector Autoregressive Processes Using Lasso,” Computational Statistics & Data Analysis, 52, 3645–3657. DOI: 10.1016/j.csda.2007.12.004.
   Web of Science ®Google Scholar
• Kock, A. B., and Callot, L. (2015), “Oracle Inequalities for High Dimensional Vector Autoregressions,” Journal of Economics, 186, 325–344. DOI: 10.1016/j.jeconom.2015.02.013.
   Google Scholar
• Krampe, J., and Margaritella, L. (2021), “Dynamic Factor Models with Sparse VAR Idiosyncratic Components,” arXiv preprint arXiv:2112.07149.
   Google Scholar
• Krampe, J., and Paparoditis, E. (2021), “Sparsity Concepts and Estimation Procedures for High-Dimensional Vector Autoregressive Models,” Journal of Time Series Analysis, 42, 554–579. DOI: 10.1111/jtsa.12586.
   Web of Science ®Google Scholar
• Lin, J., and Michailidis, G. (2020), “Regularized Estimation of High-Dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models,” Journal of Machine Learning Research, 21, 1–51.
   PubMed Web of Science ®Google Scholar
• Liu, B., Zhang, X., and Liu, Y. (2021), “Simultaneous Change Point Inference and Structure Recovery for High Dimensional Gaussian Graphical Models,” Journal of Machine Learning Research, 22, 1–62.
   Web of Science ®Google Scholar
• Loh, P.-L., and Wainwright, M. J. (2012), “High-Dimensional Regression with Noisy and Missing Data: Provable Guarantees with Nonconvexity,” The Annals of Statistics, 40, 1637–1664. DOI: 10.1214/12-AOS1018.
   Web of Science ®Google Scholar
• Lütkepohl, H. (2005), New Introduction to Multiple Time Series Analysis, Berlin: Springer.
   Google Scholar
• Masini, R. P., Medeiros, M. C., and Mendes, E. F. (2022), “Regularized Estimation of High-Dimensional Vector Autoregressions with Weakly Dependent Innovations,” Journal of Time Series Analysis, 43, 532–557. DOI: 10.1111/jtsa.12627.
   Web of Science ®Google Scholar
• Medeiros, M. C., and Mendes, E. F. (2016), “l1 -regularization of High-Dimensional Time-Series Models with Non-gaussian and Heteroskedastic Errors,” Journal of Economics, 191, 255–271.
   Google Scholar
• Nicholson, W. B., Wilms, I., Bien, J., and Matteson, D. S. (2020), “High Dimensional Forecasting via Interpretable Vector Autoregression,” Journal of Machine Learning Research, 21, 1–52.
   PubMed Web of Science ®Google Scholar
• Onatski, A. (2012), “Asymptotics of the Principal Components Estimator of Large Factor Models with Weakly Influential Factors,” Journal of Economics, 168, 244–258. DOI: 10.1016/j.jeconom.2012.01.034.
   Google Scholar
• Owens, D., Cho, H., and Barigozzi, M. (2023), “fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling,” The R Journal (to appear).
   Google Scholar
• Priestley, M. (1982), Spectral Analysis and Time Series, London: Academic Press.
   Google Scholar
• Stock, J. H., and Watson, M. W. (2002), “Forecasting Using Principal Components from a Large Number of Predictors,” Journal of the American Statistical Association, 97, 1167–1179. DOI: 10.1198/016214502388618960.
   Web of Science ®Google Scholar
• Tardivel, P. J., and Bogdan, M. (2022), “On the Sign Recovery by Least Absolute Shrinkage and Selection Operator, Thresholded Least Absolute Shrinkage and Selection Operator, and Thresholded Basis Pursuit Denoising,” Scandinavian Journal of Statistics, 49, 1636–1668. DOI: 10.1111/sjos.12568.
   Web of Science ®Google Scholar
• Uematsu, Y., and Yamagata, T. (2023), “Discovering the Network Granger Causality in Large Vector Autoregressive Models,” arXiv preprint arXiv:2303.15158.
   Google Scholar
• van de Geer, S., Bühlmann, P., and Zhou, S. (2011), “The Adaptive and the Thresholded Lasso for Potentially Misspecified Models,” Electronic Journal of Statistics, 5, 688–749. DOI: 10.1214/11-EJS624.
   Web of Science ®Google Scholar
• Wang, D., and Tsay, R. S. (2022), “Rate-Optimal Robust Estimation of High-Dimensional Vector Autoregressive Models,” arXiv preprint arXiv:2107.11002.
   Google Scholar
• Wong, K. C., Li, Z., and Tewari, A. (2020), “Lasso Guarantees for β-mixing Heavy-Tailed Time Series,” The Annals of Statistics, 48, 1124–1142. DOI: 10.1214/19-AOS1840.
   Web of Science ®Google Scholar
• Wu, W.-B., and Wu, Y. N. (2016), “Performance Bounds for Parameter Estimates of High-Dimensional Linear Models with Correlated Errors,” Electronic Journal of Statistics, 10, 352–379. DOI: 10.1214/16-EJS1108.
   Web of Science ®Google Scholar
• Zhang, D., and Wu, W. B. (2021), “Convergence of Covariance and Spectral Density Estimates for High-Dimensional Locally Stationary Processes,” The Annals of Statistics, 49, 233–254. DOI: 10.1214/20-AOS1954.
   Web of Science ®Google Scholar
• Zhao, P., and Yu, B. (2006), “On Model Selection Consistency of Lasso,” Journal of Machine Learning Research, 7, 2541–2563.
   Web of Science ®Google Scholar