Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters
R/C++ implementation of the Primiceri (2005) model, which allows for both stochastic volatility and time-varying regression parameters. The package contains functions for computing posterior predictive distributions and impulse responses from the model, based on an input data set.
| Package: | bvarsv |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2014-08-14 |
| License: | GPL (>= 2) |
| URL: | https://sites.google.com/site/fk83research/code |
Fabian Krueger <Fabian.Krueger83@gmail.com>, based on Matlab code by Dimitris Korobilis (see Koop and Korobilis, 2010).
The code incorporates the recent corrigendum by Del Negro and Primiceri (2015), which points to an error in the original MCMC algorithm of Primiceri (2005).
Del Negro, M. and Primicerio, G.E. (2015). ‘Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum’, Review of Economic Studies 82, 1342-1345.
Koop, G. and D. Korobilis (2010): ‘Bayesian Multivariate Time Series Methods for Empirical Macroeconomics’, Foundations and Trends in Econometrics 3, 267-358. Accompanying Matlab code available at https://sites.google.com/site/dimitriskorobilis/matlab.
Primiceri, G.E. (2005): ‘Time Varying Structural Vector Autoregressions and Monetary Policy’, Review of Economic Studies 72, 821-852.
## Not run: # Load US macro data data(usmacro) # Estimate trivariate model using Primiceri's prior choices (default settings) set.seed(5813) bv <- bvar.sv.tvp(usmacro) ## End(Not run)
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