Analysis of Integrated and Cointegrated Time Series with R

By B. Pfaff. 2005

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VAR, SVAR and SVEC Models: Implementation Within R Package vars (2008, background): “jo() contained in urca for more details (Pfaff 2006):”
How well do network models predict observations? On the importance of predictability in network models (2016, methodology): “For time-series data we use the Vector Autoregressive (VAR) model, which is a popular model for multivariate time series in many disciplines [see e.g. 20, 32].”
Threshold cointegration: overview and implementation in R (2013, background): “The ﬁrst one was advocated by Engle and Granger (hereafter E-G), who propose a two-steps approach, estimating the cointegrating values in the long-run representation and then plugging those estimates one the VECM representation 2 .”
Longevity risk management for life and variable annuities: The effectiveness of static hedging using longevity bonds and derivatives (2011, methodology): “The cointegration analysis and estimation was performed in R using the packages ‘urca’ and ‘vars’ written by Pfaff (2008) [38].”
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015, methodology): “The standard VAR model is defined with a Gaussian noise process, such that the model can be split up into p conditional Gaussian distributions (see, e.g., Hamilton 1994; Pfaff 2008a).”