knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
bvartools
comes with the functionality to set up and produce posterior draws for multiple models in an effort to reduce the time required for this potentially laborious process. This vignette illustrates how the package can be used to set up multiple models, produce prior specifications, obtain posterior draws and select the model with the best fit in a few steps.
For this illustrations the data set E1 from Lütkepohl (2006) is used. It contains data on West German fixed investment, disposable income and consumption expenditures in billions of DM from 1960Q1 to 1982Q4. Like in the textbook only the log-differenced series up to 1978Q4 are used.
library(bvartools) set.seed(123456) # Set seed for reproducibility data("e1") # Load data data <- diff(log(e1)) * 100 # Obtain log-differences # Use date up to 1978Q4 data <- window(data, end = c(1978, 4)) # Plot plot(data)
Functions gen_var
can be used to obtain a list of different model specifications. In the following example five models with an intercept and increasing lag orders are generated.
object <- gen_var(data, p = 0:4, deterministic = "const", iterations = 5000, burnin = 1000)
All objects use the same amounts of available observations to ensure consistency for the calculation of information criteria for model selection.
Function add_priors
can be used to produce priors for each of the models in object models
.
object <- add_priors(object, coef = list(v_i = 0, v_i_det = 0), sigma = list(df = "k", scale = 0.0001))
Posterior draws can be obtained using function draw_posterior
. The function allows to specify the number of CPUs, which are available for parallel computing.
object <- draw_posterior(object, mc.cores = 3)
object <- draw_posterior(object)
If multiple models are estimated the function produces an object of class bvarlist
, which is a list of objects of class bvar
. Thus, each element of the list can be used for further analysis.
If function summary
is applied to an object of class bvarlist
, it produces a table of information criteria for each specification. The information criteria are calculated based on the posterior draws of the respective model and calculated in the following way:
$K$ is the number of endogenous variables and $p$ the lag order of the model. If exogenous variables were used $M$ is the number of stochastic exogenous regressors and $s$ is the lag order for those variables. $N$ is the number of deterministic terms.
summary(object)
Since all information criteria have the lowest value for the model with $p = 2$, the third element of object
is used for further analyis.
plot(irf(object[[3]], impulse = "income", response = "cons", n.ahead = 10))
Chan, J., Koop, G., Poirier, D. J., & Tobias, J. L. (2019). Bayesian Econometric Methods (2nd ed.). Cambridge: University Press.
Lütkepohl, H. (2006). New introduction to multiple time series analysis (2nd ed.). Berlin: Springer.
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