`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
<- diff(log(e1)) * 100 # Obtain log-differences
data
# Use date up to 1978Q4
<- window(data, end = c(1978, 4))
data
# 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.

```
<- gen_var(data, p = 0:4,
object 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`

.

```
<- add_priors(object,
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.

`<- draw_posterior(object, mc.cores = 3) 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:

*Log-likelihood*: \(LL = \frac{1}{R} \sum_{i = 1}^{R} \left( \sum_{t = 1}^{T} -\frac{K}{2} \ln 2\pi - \frac{1}{2} \ln |\Sigma_t^{(i)}| -\frac{1}{2} (u_t^{{(i)}\prime} (\Sigma_t^{(i)})^{-1} u_t^{(i)} \right)\) for each draw \(i\) and \(u_t = y_t - \mu_t\);*Akaika information criterion*: \(AIC = 2 (Kp + M (s + 1) + N) - 2 LL\);*Bayesian information criterion*: \(BIC = ln(T) (Kp + M (s + 1) + N) - 2 LL\);*Hannan-Quinn information criterion*: \(HQ = 2 ln(ln(T)) (Kp + M (s + 1) + N) - 2 LL\).

\(K\) 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)
#> p s LL AIC BIC HQ
#> 1 0 0 -420.5955 843.1909 845.4536 844.0907
#> 2 1 0 -413.5161 835.0323 844.0830 838.6314
#> 3 2 0 -405.9250 825.8500 841.6887 832.1485
#> 4 3 0 -408.6642 837.3284 859.9552 846.3264
#> 5 4 0 -406.7913 839.5825 868.9974 851.2799
```

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.