| 參考文獻: | Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In Selected papers of hirotugu akaike, pages 199–213. Springer.
Amblard, P.-O. and Michel, O. J. (2011). On directed information theory and granger
causality graphs. Journal of computational neuroscience, 30(1):7–16.
Anderson, T. W. (1963). Asymptotic theory for principal component analysis. The Annals
of Mathematical Statistics, 34(1):122–148.
Andrews, D. W. (1987). Asymptotic results for generalized wald tests. Econometric
Theory, 3(3):348–358.
Basu, S., Shojaie, A., and Michailidis, G. (2015). Network granger causality with inherent
grouping structure. The Journal of Machine Learning Research, 16(1):417–453.
Boudjellaba, H., Dufour, J.-M., and Roy, R. (1992). Testing causality between two vectors in multivariate autoregressive moving average models. Journal of the American
Statistical Association, 87(420):1082–1090.
Dagenais, M. G. and Dufour, J.-M. (1991). Invariance, nonlinear models, and asymptotic
tests. Econometrica: Journal of the Econometric Society, pages 1601–1615.
Davis, R. A., Zang, P., and Zheng, T. (2016). Sparse vector autoregressive modeling.
Journal of Computational and Graphical Statistics, 25(4):1077–1096.
Dufour, J.-M. (2003). Identification, weak instruments, and statistical inference in econometrics. Canadian Journal of Economics/Revue canadienne d’économique, 36(4):767–
808.
Dufour, J.-M., Pelletier, D., and Renault, É. (2006). Short run and long run causality in
time series: inference. Journal of Econometrics, 132(2):337–362.
Dufour, J.-M. and Renault, E. (1998). Short run and long run causality in time series:
theory. Econometrica, pages 1099–1125.
Dufour, J.-M., Renault, E., and Zinde-Walsh, V. (2013). Wald tests when restrictions are
locally singular. arXiv preprint arXiv:1312.0569.
Ferguson, T. S. (2017). A course in large sample theory. Routledge.
Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications.
Granger, C. W. (1969). Investigating causal relations by econometric models and crossspectral methods. Econometrica: journal of the Econometric Society, pages 424–438.
Hannan, E. J. and Quinn, B. G. (1979). The determination of the order of an autoregression.
Journal of the Royal Statistical Society: Series B (Methodological), 41(2):190–195.
Hung, Y.-C., Tseng, N.-F., and Balakrishnan, N. (2014). Trimmed granger causality between two groups of time series. Electronic Journal of Statistics, 8(2):1940–1972.
Lozano, A. C., Abe, N., Liu, Y., and Rosset, S. (2009). Grouped graphical granger modeling for gene expression regulatory networks discovery. Bioinformatics, 25(12):i110–
i118.
Lütkepohl, H. (2000). Bootstrapping impulse responses in var analyses. In COMPSTAT,
pages 109–119. Springer.
Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer Science
& Business Media.
Lütkepohl, H. and Burda, M. M. (1997). Modified wald tests under nonregular conditions.
Journal of Econometrics, 78(2):315–332.
Quinn, B. G. (1980). Order determination for a multivariate autoregression. Journal of
the Royal Statistical Society: Series B (Methodological), 42(2):182–185.
Ratsimalahelo, Z. (2005). Generalised wald type tests of nonlinear restrictions. IFAC
Proceedings Volumes, 38(1):100–105.
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, pages
461–464.
Sims, C. A. (1980). Macroeconomics and reality. Econometrica: journal of the Econometric Society, pages 1–48.
Tank, A., Covert, I., Foti, N., Shojaie, A., and Fox, E. (2018). Neural granger causality.
arXiv preprint arXiv:1802.05842.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the
Royal Statistical Society: Series B (Methodological), 58(1):267–288.
Vicente, R., Wibral, M., Lindner, M., and Pipa, G. (2011). Transfer entropy—a modelfree measure of effective connectivity for the neurosciences. Journal of computational
neuroscience, 30(1):45–67.
Von Weyl, H. (1909). Über beschränkte quadratische formen, deren differenz vollstetig
ist. Rendiconti del Circolo Matematico di Palermo (1884-1940), 27(1):373–392.
Xiao, H. and Wu, W. B. (2012). Covariance matrix estimation for stationary time series.
The Annals of Statistics, 40(1):466–493.
Yuen, T., Wong, H., and Yiu, K. F. C. (2018). On constrained estimation of graphical time
series models. Computational Statistics & Data Analysis, 124:27–52. |
