VAR, SVAR and SVEC Models: Implementation Within R Package vars
By B. Pfaff. 2008
535 citations recorded by the source corpus.
The structure of the package vars and its implementation of vector autoregressive, structural vector autoregressive and structural vector error correction models are explained in this paper. In addition to the three cornerstone functions VAR(), SVAR() and SVEC() for estimating such models, functions for diagnostic testing, estimation of a restricted models, prediction, causality analysis, impulse response analysis and forecast error variance decomposition are provided too. It is further possible to convert vector error correction models into their level VAR representation. The different methods and functions are elucidated by employing a macroeconomic data set for Canada. However, the focus in this writing is on the implementation part rather than the usage of the tools at hand.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/e7ee6ba2cb9b9728a1c93bfa4068ab229a47bb23
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In Economics and Econometrics
Citations in the corpus
- Granger Causality: A Review and Recent Advances (2021, methodology): “Bivariate VAR modeling using the vars R-package (Pfaff, 2008) confirms these observations.”
- mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015, methodology): “For VAR models, the vars package Pfaff (2008b) allows to fit VAR models with Gaussian noise.”