Abstract

This document describes the `BGVAR`

library to estimate
Bayesian Global vector autoregressions (GVAR) with different prior
specifications and stochastic volatility. The library offers a fully
fledged toolkit to conduct impulse response functions, forecast error
variance and historical error variance decompositions. To identify
structural shocks in a given country model or joint regional shocks, the
library offers simple Cholesky decompositions, generalized impulse
response functions and zero and sign restrictions – the latter of which
can also be put on the cross-section. We also allow for different
structures of the GVAR like including different weights for different
variables or setting up additional country models that determine global
variables such as oil prices. Last, we provide functions to conduct and
evaluate out-of-sample forecasts as well as conditional forecasts that
allow for the setting of a future path for a particular variable of
interest. The toolbox requires `R>=3.5`

.

This vignette describes the BGVAR package that allows for the estimation of Bayesian global vector autoregressions (GVARs). The focus of the vignette is to provide a range of examples that demonstrate the full functionality of the library. It is accompanied by a more technical description of the GVAR framework. Here, it suffices to briefly summarize the main idea of a GVAR, which is a large system of equations designed to analyze or control for interactions across units. Most often, these units refer to countries and the interactions between them arise through economic and financial interdependencies. Also in this document, the examples we provide contain cross-country data. In principle, however, the GVAR framework can be applied to other units, such as regions, firms, etc. The following examples show how the GVAR can be used to either estimate spillover effects from one country to another, or alternatively, to look at the effects of a domestic shock controlling for global factors.

In a nutshell, the GVAR consists of two stages. In the first, \(N\) vector autoregressive (VAR) models are
estimated, one per unit. Each equation in a unit model is augmented with
foreign variables, that control for global factors and link the
unit-specific models later. Typically, these foreign variables are
constructed using exogenous, bilateral weights, stored in an \(N \times N\) weight matrix. The classical
framework of Pesaran, Schuermann, and Weiner
(2004) and Dees et al. (2007)
proposes estimating these country models in vector error correction
form, while in this package we take a Bayesian stance and estimation is
carried out using VARs. The user can transform the data prior estimation
into stationary form or estimate the model in levels. The
`BGVAR`

package also allows us to include a trend to get
trend-stationary data. In the second step, the single country models are
combined using the assumption that single models are linked via the
exogenous weights, to yield a global representation of the model. This
representation of the model is then used to carry out impulse response
analysis and forecasting.

This vignette consists of four blocks: getting started and data
handling, estimation, structural analysis and forecasting. In the next
part, we discuss which data formats the `bgvar`

library can
handle. We then proceed by showing examples of how to estimate a model
using different Bayesian shrinkage priors – for references see Crespo Cuaresma, Feldkircher, and Huber (2016)
and Martin Feldkircher and Huber (2016).
We also discuss how to run diagnostic and convergence checks and examine
the main properties of the model. In the third section, we turn to
structural analysis, either using recursive (Cholesky) identification or
sign restrictions. We will also discuss structural and generalized
forecast error variance decompositions and historical decompositions. In
the last section, we show how to compute unconditional and conditional
forecasts with the package.

We start by installing the package from CRAN and attaching it with

```
<- par(no.readonly=TRUE)
oldpar set.seed(123)
library(BGVAR)
```

To ensure reproducibility of the examples that follow, we have set a
particular seed (for `R`

s random number generator). As every
`R`

library, the `BGVAR`

package provides built-in
help files which can be accessed by typing `?`

followed by
the function / command of interest. It also comes along with four
example data sets, two of them correspond to data the quarterly data set
used in Martin Feldkircher and Huber
(2016) (`eerData`

, `eerDataspf`

), one is on
monthly frequency (`monthlyData`

). For convenience we also
include the data that come along with the Matlab GVAR toolbox of Smith, L.V. and Galesi, A. (2014),
`pesaranData`

. We include the 2019 vintage (K. Mohaddes and Raissi 2020).

We start illustrating the functionality of the `BGVAR`

package by using the `eerData`

data set from Martin Feldkircher and Huber (2016). It contains
76 quarterly observations for 43 countries over the period from 1995Q1
to 2013Q4. The euro area (EA) is included as a regional aggregate.

We can load the data by typing

`data(eerData)`

This loads two objects: `eerData`

, which is a list object of
length \(N\) (i.e., the number of
countries) and `W.trade0012`

, which is an \(N \times N\) weight matrix.

We can have a look at the names of the countries contained in
`eerData`

`names(eerData)`

```
## [1] "EA" "US" "UK" "JP" "CN" "CZ" "HU" "PL" "SI" "SK" "BG" "RO" "EE" "LT" "LV"
## [16] "HR" "AL" "RS" "RU" "UA" "BY" "GE" "AR" "BR" "CL" "MX" "PE" "KR" "PH" "SG"
## [31] "TH" "IN" "ID" "MY" "AU" "NZ" "TR" "CA" "CH" "NO" "SE" "DK" "IS"
```

and at the names of the variables contained in a particular country by

`colnames(eerData$UK)`

`## [1] "y" "Dp" "rer" "stir" "ltir" "tb"`

We can zoom in into each country by accessing the respective slot of the data list:

`head(eerData$US)`

```
## y Dp rer stir ltir tb poil
## [1,] 4.260580 0.007173874 4.535927 0.0581 0.0748 -0.010907595 2.853950
## [2,] 4.262318 0.007341077 4.483116 0.0602 0.0662 -0.010637081 2.866527
## [3,] 4.271396 0.005394799 4.506013 0.0580 0.0632 -0.007689327 2.799958
## [4,] 4.278025 0.006218849 4.526343 0.0572 0.0589 -0.008163675 2.821479
## [5,] 4.287595 0.007719866 4.543933 0.0536 0.0591 -0.008277170 2.917315
## [6,] 4.301597 0.008467671 4.543933 0.0524 0.0672 -0.009359032 2.977115
```

Here, we see that the global variable, oil prices (`poil`

) is
attached to the US country model. This corresponds to the classical GVAR
set-up used among others in Pesaran, Schuermann,
and Weiner (2004) and Dees et al.
(2007). We also see that in general, each country model \(i\) can contain a different set of
variables \(k_i\) as opposed to
requirements in a balanced panel.

The GVAR toolbox relies on one important *naming convention*,
though: It is assumed that neither the country names nor the variable
names contain a `.`

[dot]. The reason is that the program
internally has to collect and separate the data more than once and in
doing that, it uses the `.`

to separate countries / entities
from variables. To give a concrete example, the slot in the
`eerData`

list referring to the USA should not be labelled
`U.S.A.`

, nor should any of the variable names contain a
`.`

The toolbox also allows the user to submit the data as a \(T \times k\) data matrix, with \(k=\sum^N_{i=1} k_i\) denoting the sum of
endogenous variables in the system. We can switch from data
representation in list form to matrix from by using the function
`list_to_matrix`

(and vice versa using
`matrix_to_list`

).

To convert the `eerData`

we can type:

`<-list_to_matrix(eerData) bigX`

For users who want to submit data in matrix form, the above mentioned
naming convention implies that the column names of the data matrix have
to include the name of the country / entity and the variable name,
separated by a `.`

For example, for the converted
`eerData`

data set, the column names look like:

`colnames(bigX)[1:10]`

```
## [1] "EA.y" "EA.Dp" "EA.rer" "EA.stir" "EA.ltir" "EA.tb" "US.y"
## [8] "US.Dp" "US.rer" "US.stir"
```

with the first part of each columname indicating the country (e.g.,
`EA`

) and the second the variable (e.g., `y`

),
separated by a `.`

Regardless whether the data are submitted
as list or as big matrix, the underlying data can be either of
`matrix`

class or time series classes such as `ts`

or `xts`

.

Finally, we look at the second important ingredient to build our GVAR
model, the weight matrix. Here, we use annual bilateral trade flows
(including services), averaged over the period from 2000 to 2012. This
implies that the \(ij^{th}\) element of
\(W\) contains trade flows from unit
\(i\) to unit \(j\). These weights can also be made
symmetric by calculating \(\frac{(W_{ij}+W_{ji})}{2}\). Using trade
weights to establish the links in the GVAR goes back to the early GVAR
literature (Pesaran, Schuermann, and Weiner
2004) but is still used in the bulk of GVAR studies. Other
weights, such as financial flows, have been proposed in Eickmeier and Ng (2015) and examined in Martin Feldkircher and Huber (2016). Another
approach is to use estimated weights as in Martin
Feldkircher and Siklos (2019). The weight matrix should have
`rownames`

and `colnames`

that correspond to the
\(N\) country names contained in
`Data`

.

`head(W.trade0012)`

```
## EA US UK JP CN CZ
## EA 0.0000000 0.13815804 0.16278169 0.03984424 0.09084817 0.037423312
## US 0.1666726 0.00000000 0.04093296 0.08397042 0.14211997 0.001438531
## UK 0.5347287 0.11965816 0.00000000 0.02628600 0.04940218 0.008349458
## JP 0.1218515 0.21683444 0.02288576 0.00000000 0.22708532 0.001999762
## CN 0.1747925 0.19596384 0.02497009 0.15965721 0.00000000 0.003323641
## CZ 0.5839067 0.02012227 0.03978617 0.01174212 0.03192080 0.000000000
## HU PL SI SK BG RO
## EA 0.026315925 0.046355019 0.0088805499 0.0140525286 0.0054915888 0.0147268739
## US 0.001683935 0.001895003 0.0003061785 0.0005622383 0.0002748710 0.0007034870
## UK 0.006157917 0.012682611 0.0009454295 0.0026078946 0.0008369228 0.0031639564
## JP 0.002364775 0.001761420 0.0001650431 0.0004893263 0.0001181310 0.0004293428
## CN 0.003763771 0.004878752 0.0005769658 0.0015252866 0.0006429077 0.0019212312
## CZ 0.025980933 0.062535144 0.0058429207 0.0782762640 0.0027079942 0.0080760690
## EE LT LV HR AL
## EA 0.0027974288 3.361644e-03 1.857555e-03 0.0044360005 9.328127e-04
## US 0.0002678272 4.630261e-04 2.407372e-04 0.0002257508 2.057213e-05
## UK 0.0009922865 1.267497e-03 1.423142e-03 0.0004528439 3.931010e-05
## JP 0.0002038361 9.363053e-05 9.067431e-05 0.0001131534 4.025852e-06
## CN 0.0004410996 5.033345e-04 4.041432e-04 0.0006822057 1.435258e-04
## CZ 0.0009807047 2.196688e-03 1.107030e-03 0.0027393932 1.403948e-04
## RS RU UA BY GE AR
## EA 2.430815e-03 0.06112681 0.0064099317 1.664224e-03 0.0003655903 0.005088057
## US 7.079076e-05 0.01024815 0.0008939856 2.182909e-04 0.0001843835 0.004216105
## UK 2.730431e-04 0.01457675 0.0010429571 4.974630e-04 0.0001429294 0.001387324
## JP 2.951168e-05 0.01725841 0.0007768546 4.392221e-05 0.0001062724 0.001450359
## CN 1.701277e-04 0.02963668 0.0038451574 5.069157e-04 0.0001685347 0.005817928
## CZ 1.638111e-03 0.03839817 0.0082099438 1.447486e-03 0.0002651330 0.000482138
## BR CL MX PE KR PH
## EA 0.018938315 0.0051900915 0.011455138 0.0019463003 0.018602006 0.0034965601
## US 0.020711342 0.0064996880 0.140665729 0.0037375799 0.033586979 0.0076270807
## UK 0.007030657 0.0016970182 0.003832710 0.0005204850 0.010183755 0.0025279396
## JP 0.010910951 0.0077091104 0.011164908 0.0020779123 0.079512267 0.0190560664
## CN 0.025122526 0.0102797021 0.010960261 0.0040641411 0.103135774 0.0150847984
## CZ 0.001898955 0.0002425703 0.001538938 0.0001152155 0.005248484 0.0008790869
## SG TH IN ID MY AU
## EA 0.012319690 0.007743377 0.016629452 0.0065409485 0.009631702 0.010187442
## US 0.017449474 0.012410910 0.014898397 0.0079866535 0.017364286 0.011578348
## UK 0.011309096 0.006146707 0.013461838 0.0032341466 0.006768142 0.012811822
## JP 0.029885052 0.044438961 0.010319951 0.0369586674 0.035612054 0.047921306
## CN 0.029471018 0.024150334 0.024708981 0.0201353186 0.033336788 0.036066785
## CZ 0.002867306 0.003136170 0.003568422 0.0009949029 0.002695855 0.001291178
## NZ TR CA CH NO SE
## EA 0.0017250647 0.028935117 0.012886035 0.065444998 0.025593188 0.041186900
## US 0.0023769166 0.004969085 0.213004968 0.013343786 0.003975441 0.006793041
## UK 0.0022119334 0.013099627 0.020699895 0.026581778 0.033700469 0.022629934
## JP 0.0048098149 0.002560745 0.020149431 0.010014273 0.002895884 0.004375537
## CN 0.0030625686 0.006578520 0.019121787 0.007657646 0.002804712 0.005853722
## CZ 0.0001703354 0.006490032 0.001808704 0.013363416 0.003843421 0.013673889
## DK IS
## EA 0.025035373 1.163498e-03
## US 0.003135419 2.750740e-04
## UK 0.013518119 1.119179e-03
## JP 0.003207880 2.637944e-04
## CN 0.003990731 7.806304e-05
## CZ 0.007521567 1.490167e-04
```

The countries in the weight matrix should be in the same order as in the data list:

`all(colnames(W.trade0012)==names(eerData))`

`## [1] TRUE`

The weight matrix should be row-standardized and the diagonal elements should be zero:

`rowSums(W.trade0012)`

```
## EA US UK JP CN CZ HU PL SI SK BG RO EE LT LV HR AL RS RU UA BY GE AR BR CL MX
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## PE KR PH SG TH IN ID MY AU NZ TR CA CH NO SE DK IS
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
```

`diag(W.trade0012)`

```
## EA US UK JP CN CZ HU PL SI SK BG RO EE LT LV HR AL RS RU UA BY GE AR BR CL MX
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## PE KR PH SG TH IN ID MY AU NZ TR CA CH NO SE DK IS
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
```

Note that through row-standardizing, the final matrix is typically not symmetric (even when using the symmetric weights as raw input).

In what follows, we restrict the dataset to contain only three
countries, `EA`

, `US`

and `RU`

and
adjust the weight matrix accordingly. We do this only for
*illustrational purposes to save time and storage in this
document*:

```
<-c("EA","US","RU")
cN<-eerData[cN]
eerData<-W.trade0012[cN,cN]
W.trade0012<-apply(W.trade0012,2,function(x)x/rowSums(W.trade0012))
W.trade0012<-lapply(W.list,function(l){l<-apply(l[cN,cN],2,function(x)x/rowSums(l[cN,cN]))}) W.list
```

This results in the same dataset as available in
`testdata`

.

In order to make BGVAR easier to handle for users working and organising
data in spreadsheets via Excel, we provide a own reader function relying
on the `readxl`

package. In this section we intend to provide
some code to write the provided datasets to Excel spreadsheets, and to
show then how to read the data from Excel. Hence, we provide an
easy-to-follow approach with an example how the data should be organised
in Excel.

We start by exporting the data to excel. The spreadsheet should be
organised as follows. Each sheet consists of the data set for one
particular country, hence the naming of the sheets with the country
names is essential. In each sheet, you should provide the time in the
first column of the matrix, followed by one column per variable. In the
following, we will export the `eerData`

data set to
Excel:

```
<- as.character(seq.Date(as.Date("1995-01-01"),as.Date("2013-10-01"),by="quarter"))
time
for(cc in 1:length(eerData)){
<- coredata(eerData[[cc]])
x rownames(x) <- time
write.xlsx(x = x, file="./excel_eerData.xlsx", sheetName = names(eerData)[cc],
col.names=TRUE, row.names=TRUE, append=TRUE)
}
```

which will create in your current working directory an excel sheet
named `excel_eerData.xlsx`

. This can then be read to R with
the `BGVAR`

package as follows:

`<- excel_to_list(file = "./excel_eerData.xlsx", first_column_as_time=TRUE, skipsheet=NULL, ...) eerData_read `

which creates a list in the style of the original
`eerData`

data set. The first argument `file`

has
to be valid path to an excel file. The second argument
`first_column_as_time`

is a logical indicating whether you
provide as first column in each spreadsheet a time index, while the
`skipsheet`

argument can be specified to leave out specific
sheets (either as vector of strings or numeric indices). If you want to
transform the list object to a matrix, you can use the command
`list_to_matrix`

or to transform it back to a list with
`matrix_to_list`

:

```
<- list_to_matrix(eerData_read)
eerData_matrix <- matrix_to_list(eerData_matrix) eerData_list
```

The main function of the `BGVAR`

package is its
`bgvar`

function. The unique feature of this toolbox is that
we use Bayesian shrinkage priors with optionally stochastic volatility
to estimate the country models in the GVAR. In its current version,
three priors for the country VARs are implemented:

- Non-conjugate Minnesota prior (
`MN`

, Litterman 1986; Koop and Korobilis 2010) - Stochastic Search Variable Selection prior (
`SSVS`

, George, Sun, and Ni 2008) - Normal-Gamma prior (
`NG`

, Huber and Feldkircher 2019)

The first two priors are described in more detail in Crespo Cuaresma, Feldkircher, and Huber (2016).
For a more technical description of the Normal-Gamma prior see Huber and Feldkircher (2019) and for an
application in the GVAR context Martin
Feldkircher and Siklos (2019). For the variances we can assume
homoskedasticity or time variation (stochastic volatility). For the
latter, the library relies on the `stochvol`

package of Kastner (2016).

We start with estimating our toy model using the `NG`

prior, the reduced `eerData`

data set and the adjusted
`W.trade0012`

weight matrix:

```
.1<-bgvar(Data=eerData,
modelW=W.trade0012,
draws=100,
burnin=100,
plag=1,
prior="NG",
hyperpara=NULL,
SV=TRUE,
thin=1,
trend=TRUE,
hold.out=0,
eigen=1
)
```

The default prior specification in `bgvar`

is to use the NG
prior with stochastic volatility and one lag for both the endogenous and
weakly exogenous variables (`plag=1`

). In general, due to its
high cross-sectional dimension, the GVAR can allow for very complex
univariate dynamics and it might thus not be necessary to increase the
lag length considerably as in a standard VAR (Burriel and Galesi 2018). The setting
`hyperpara=NULL`

implies that we use the standard
hyperparameter specification for the NG prior; see the helpfiles for
more details.

Other standard specifications that should be submitted by the user
comprise the number of posterior draws (`draws`

) and burn-ins
(`burnin`

, i.e., the draws that are discarded). To ensure
that the MCMC estimation has converged, a high-number of burn-ins is
recommended (say 15,000 to 30,000). Saving the full set of posterior
draws can eat up a lot of storage. To reduce this, we can use a thinning
interval which stores only a thin\(^{th}\) draw of the global posterior
output. For example, with `thin=10`

and
`draws=5000`

posterior draws, the amount of MCMC draws stored
is 500. `TREND=TRUE`

implies that the model is estimated
using a trend. Note that regardless of the trend specification, each
equation always automatically includes an intercept term.

Expert users might want to take further adjustments. These have to be
provided via a list (`expert`

). For example, to speed up
computation, it is possible to invoke parallel computing in
`R`

. The number of available cpu cores can be specified via
`cores`

. Ideally this number is equal to the number of units
\(N\)
(`expert=list(cores=N)`

). Based on the user’s operating
system, the package then either uses `parLapply`

(Windows
platform) or `mclapply`

(non-Windows platform) to invoke
parallel computing. If `cores=NULL`

, the unit models are
estimated subsequently in a loop (via `R`

’s
`lapply`

function). To use other / own apply functions, pass
them on via the argument `applyfun`

. As another example, we
might be interested in inspecting the output of the \(N\) country models in more detail. To do
so, we could provide `expert=list(save.country.store=TRUE)`

,
which allows to save the whole posterior distribution of each unit /
country model. Due to storage reasons, the default is set to
`FALSE`

and only the *posterior medians* of the single
country models are reported. Note that even in this case, the whole
posterior distribution of the *global model* is stored.

We estimated the above model with stochastic volatility
(`SV=TRUE`

). There are several reasons why one may want to
let the residual variances change over time. First and foremost, most
time periods used in macroeconometrics are nowadays rather volatile
including severe recessions. Hence accounting for time variation might
improve the fit of the model (Primiceri 2005;
Sims and Zha 2006; Dovern, Feldkircher, and Huber 2016; Huber
2016). Second, the specification implemented in the toolbox nests
the homoskedastic case. It is thus a good choice to start with the more
general case when first confronting the model with the data. For
structural analysis such as the calculation of impulse responses, we
take the variance covariance matrix with the median volatilities (over
the sample period) on its diagonal. If we want to look at the
volatilities of the first equation (`y`

) in the euro area
country model, we can type:

`.1$cc.results$sig$EA[,"EA.y","EA.y"] model`

To discard explosive draws, we can compute the eigenvalues of the
reduced form of the global model, written in its companion form.
Unfortunately, this can only be done once the single models have been
estimated and stacked together (and hence not directly built into the
MCMC algorithm for the country models). To discard draws that lead to
higher eigenvalues than 1.05, set `eigen=1.05`

. We can look
at the 10 largest eigenvalues by typing:

`.1$stacked.results$F.eigen[1:10] model`

```
## [1] 0.9840958 0.9871514 0.9748570 0.9809818 0.9670457 0.9741479 0.9932895
## [8] 0.9847488 0.9645665 0.9640903
```

Last, we have used the default option `h=0`

, which implies
that we use the full sample period to estimate the GVAR. For the purpose
of forecast evaluation, `h`

could be specified to a positive
number, which then would imply that the last `h`

observations
are reserved as a hold-out sample and not used to estimate the model.

Having estimated the model, we can summarize the outcome in various ways.

First, we can use the`print`

method
`print(model.1)`

```
## ---------------------------------------------------------------------------
## Model Info:
## Prior: Normal-Gamma prior (NG)
## Number of lags for endogenous variables: 1
## Number of lags for weakly exogenous variables: 1
## Number of posterior draws: 100/1=100
## Size of GVAR object: 0.6 Mb
## Trimming leads to 39 (39%) stable draws out of 100 total draws.
## ---------------------------------------------------------------------------
## Model specification:
##
## EA: y, Dp, rer, stir, ltir, tb, y*, Dp*, rer*, stir*, ltir*, tb*, poil**, trend
## US: y, Dp, rer, stir, ltir, tb, poil, y*, Dp*, rer*, stir*, ltir*, tb*, trend
## RU: y, Dp, rer, stir, tb, y*, Dp*, rer*, stir*, ltir*, tb*, poil**, trend
```

This just prints the submitted arguments of the `bgvar`

object along with the model specification for each unit. The asterisks
indicate weakly exogenous variables, double asterisks exogenous
variables and variables without asterisks the endogenous variables per
unit.

The `summary`

method is a more enhanced way to analyze
output. It computes descriptive statistics like convergence properties
of the MCMC chain, serial autocorrelation in the errors and the average
pairwise autocorrelation of cross-unit residuals.

`summary(model.1) `

```
## ---------------------------------------------------------------------------
## Model Info:
## Prior: Normal-Gamma prior (NG)
## Number of lags for endogenous variables: 1
## Number of lags for weakly exogenous variables: 1
## Number of posterior draws: 100/1=100
## Number of stable posterior draws: 39
## Number of cross-sectional units: 3
## ---------------------------------------------------------------------------
## Convergence diagnostics
## Geweke statistic:
## 127 out of 360 variables' z-values exceed the 1.96 threshold (35.28%).
## ---------------------------------------------------------------------------
## F-test, first order serial autocorrelation of cross-unit residuals
## Summary statistics:
## ========= ========== ======
## \ # p-values in %
## ========= ========== ======
## >0.1 5 27.78%
## 0.05-0.1 1 5.56%
## 0.01-0.05 5 27.78%
## <0.01 7 38.89%
## ========= ========== ======
## ---------------------------------------------------------------------------
## Average pairwise cross-unit correlation of unit-model residuals
## Summary statistics:
## ======= ======== ======== ========== ========== ======== ==========
## \ y Dp rer stir ltir tb
## ======= ======== ======== ========== ========== ======== ==========
## <0.1 0 (0%) 3 (100%) 2 (66.67%) 1 (33.33%) 2 (100%) 1 (33.33%)
## 0.1-0.2 0 (0%) 0 (0%) 0 (0%) 1 (33.33%) 0 (0%) 2 (66.67%)
## 0.2-0.5 3 (100%) 0 (0%) 1 (33.33%) 1 (33.33%) 0 (0%) 0 (0%)
## >0.5 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
## ======= ======== ======== ========== ========== ======== ==========
## ---------------------------------------------------------------------------
```

We can now have a closer look at the output provided by
`summary`

. The header contains some basic information about
the prior used to estimate the model, how many lags, posterior draws and
countries. The next line shows Geweke’s CD statistic, which is
calculated using the `coda`

package. Geweke’s CD assesses
practical convergence of the MCMC algorithm. In a nutshell, the
diagnostic is based on a test for equality of the means of the first and
last part of a Markov chain (by default we use the first 10% and the
last 50%). If the samples are drawn from the stationary distribution of
the chain, the two means are equal and Geweke’s statistic has an
asymptotically standard normal distribution.

The test statistic is a standard Z-score: the difference between the two
sample means divided by its estimated standard error. The standard error
is estimated from the spectral density at zero and so takes into account
any autocorrelation. The test statistic shows that only a small fraction
of all coefficients did not convergence. Increasing the number of
burn-ins can help decreasing this fraction further. The statistic can
also be calculated by typing `conv.diag(model.1)`

.

The next model statistic is the likelihood of the global model. This
statistic can be used for model comparison. Next and to assess, whether
there is first order serial autocorrelation present, we provide the
results of a simple F-test. The table shows the share of p-values that
fall into different significance categories. Since the null hypothesis
is that of no serial correlation, we would like to have as many large
(\(>0.1\)) p-values as possible. The
statistics show that already with one lag, serial correlation is modest
in most equations’ residuals. This could be the case since we have
estimated the unit models with stochastic volatility. To further
decrease serial correlation in the errors, one could increase the number
of lags via `plag`

.

The last part of the summary output contains a statistic of cross-unit correlation of (posterior median) residuals. One assumption of the GVAR framework is that of negligible, cross-unit correlation of the residuals. Significant correlations prohibit structural and spillover analysis (Dees et al. 2007). In this example, correlation is reasonably small.

Some other useful methods the `BGVAR`

toolbox offers contain
the `coef`

(or `coefficients`

as its alias)
methods to extract the \(k \times k \times
plag\) matrix of reduced form coefficients of the global model.
Via the `vcov`

command, we can access the global variance
covariance matrix and the `logLik()`

function allows us to
gather the global log likelihood (as provided by the
`summary`

command).

```
<- coef(model.1)
Fmat <- vcov(model.1)
Smat <- logLik(model.1) lik
```

Last, we can have a quick look at the in-sample fit using either the
posterior median of the country models’ residuals
(`global=FALSE`

) or those of the global solution of the GVAR
(`global=TRUE`

). The in-sample fit can also be extracted by
using `fitted()`

.

Here, we show the in-sample fit of the euro area model
(`global=FALSE`

).

```
<- fitted(model.1)
yfit plot(model.1, global=FALSE, resp="EA")
```

We can estimate the model with two further priors on the unit models, the SSVS prior and the Minnesota prior. To give a concrete example, the SSVS prior can be invoked by typing:

```
.1<-bgvar(Data=eerData,
model.ssvsW=W.trade0012,
draws=100,
burnin=100,
plag=1,
prior="SSVS",
hyperpara=NULL,
SV=TRUE,
thin=1,
Ex=NULL,
trend=TRUE,
expert=list(save.shrink.store=TRUE),
hold.out=0,
eigen=1,
verbose=TRUE
)
```

One feature of the SSVS prior is that it allows us to look at the
posterior inclusion probabilities to gauge the importance of particular
variables. Per default, `bgvar`

does not save the
volatilities of the coefficients to save memory. If we set
`expert=list(save.shrink.store=TRUE)`

to `TRUE`

(default is `FALSE`

) then those probabilities are saved and
posterior inclusion probabilities (PIPs) are computed. For example, we
can have a look at the PIPs of the euro area model by typing:

`.1$cc.results$PIP$PIP.cc$EA model.ssvs`

```
## y Dp rer stir ltir tb
## y_lag1 1.00 1.00 0.20 0.24 0.16 0.33
## Dp_lag1 0.31 0.29 0.37 0.29 0.35 0.14
## rer_lag1 0.18 0.13 1.00 1.00 0.22 0.36
## stir_lag1 0.45 0.15 0.26 1.00 0.34 0.87
## ltir_lag1 0.55 0.41 0.62 0.14 1.00 0.96
## tb_lag1 0.32 1.00 0.71 0.30 0.34 1.00
## y* 1.00 0.39 0.15 0.10 0.05 0.47
## Dp* 0.18 0.88 0.21 0.05 0.09 0.40
## rer* 0.40 0.23 1.00 0.33 0.19 0.26
## stir* 0.54 0.53 0.44 0.36 0.22 0.13
## ltir* 0.47 0.51 0.56 0.99 0.14 0.59
## tb* 0.23 1.00 0.30 0.43 0.31 0.11
## poil** 0.73 1.00 0.17 0.08 0.13 0.50
## y*_lag1 0.22 0.77 0.10 0.08 0.14 0.64
## Dp*_lag1 0.56 0.66 0.22 0.41 0.20 0.25
## rer*_lag1 0.41 1.00 1.00 0.23 0.20 0.21
## stir*_lag1 0.33 0.28 0.48 0.43 0.10 0.28
## ltir*_lag1 1.00 1.00 0.25 1.00 0.65 0.07
## tb*_lag1 0.59 0.25 1.00 0.21 0.19 1.00
## poil**_lag1 0.89 0.49 0.11 0.06 0.22 0.22
## cons 0.32 1.00 1.00 0.22 0.12 1.00
## trend 0.33 0.23 0.26 0.07 0.16 1.00
```

The equations in the EA country model can be read column-wise with the
rows representing the associated explanatory variables. The example
shows that besides other variables, the trade balance (`tb`

)
is an important determinant of the real exchange rate
(`rer`

).

We can also have a look at the average of the PIPs across all units:

`.1$cc.results$PIP$PIP.avg model.ssvs`

```
## y Dp rer stir ltir tb poil
## y_lag1 1.0000000 0.5966667 0.2033333 0.5166667 0.095 0.5900000 0.08
## Dp_lag1 0.3500000 0.4700000 0.2666667 0.1866667 0.260 0.2333333 0.89
## rer_lag1 0.2833333 0.3900000 1.0000000 0.4500000 0.270 0.6300000 0.54
## stir_lag1 0.6966667 0.1700000 0.3066667 1.0000000 0.385 0.4700000 0.12
## ltir_lag1 0.2950000 0.3000000 0.4050000 0.2350000 1.000 0.6650000 0.27
## tb_lag1 0.1633333 0.6800000 0.3833333 0.2966667 0.285 1.0000000 0.12
## y* 0.4666667 0.2000000 0.4200000 0.2366667 0.080 0.4866667 1.00
## Dp* 0.2066667 0.6500000 0.2133333 0.0800000 0.290 0.6433333 1.00
## rer* 0.3766667 0.2566667 0.8300000 0.2233333 0.350 0.4433333 0.20
## stir* 0.5300000 0.2800000 0.2200000 0.2866667 0.325 0.2366667 0.14
## ltir* 0.2000000 0.3866667 0.2233333 0.6566667 0.230 0.2866667 0.08
## tb* 0.4266667 0.6800000 0.2933333 0.3100000 0.255 0.2366667 0.37
## poil** 0.8650000 0.5700000 0.3200000 0.2400000 0.130 0.3900000 NaN
## y*_lag1 0.3866667 0.3333333 0.3866667 0.3766667 0.115 0.4466667 1.00
## Dp*_lag1 0.2466667 0.3166667 0.2400000 0.1933333 0.210 0.3000000 0.11
## rer*_lag1 0.6933333 0.4466667 0.7666667 0.1500000 0.405 0.1633333 0.24
## stir*_lag1 0.4500000 0.2066667 0.1900000 0.4466667 0.215 0.4066667 0.28
## ltir*_lag1 0.4300000 0.5500000 0.3666667 0.4333333 0.355 0.1466667 0.05
## tb*_lag1 0.4600000 0.3033333 0.5766667 0.2466667 0.305 0.4866667 0.08
## poil**_lag1 0.4900000 0.3400000 0.1500000 0.1100000 0.220 0.1500000 NaN
## cons 0.4466667 0.4133333 0.6000000 0.2633333 0.200 0.8333333 0.13
## trend 0.1866667 0.2933333 0.2833333 0.4433333 0.130 0.6433333 0.74
## poil_lag1 0.0500000 0.2100000 0.1400000 1.0000000 0.150 0.2200000 1.00
```

This shows that the same determinants for the real exchange rate appear as important regressors in other country models.

In this section we explore different specifications of the structure of the GVAR model. Other specification choices that relate more to the time series properties of the data, such as specifying different lags and priors are left for the reader to explore. We will use the SSVS prior and judge the different specifications by examining the posterior inclusion probabilities.

As a first modification, we could use different weights for different variable classes as proposed in Eickmeier and Ng (2015). For example we could use financial weights to construct weakly exogenous variables of financial factors and trade weights for real variables.

The `eerData`

set provides us with a list of different
weight matrices that are described in the help files.

Now we specify the sets of variables to be weighted:

`<-list();variable.list$real<-c("y","Dp","tb");variable.list$fin<-c("stir","ltir","rer") variable.list`

We can then re-estimate the model and hand over the
`variable.list`

via the argument `expert`

:

```
# weights for first variable set tradeW.0012, for second finW0711
.2<-bgvar(Data=eerData,
model.ssvsW=W.list[c("tradeW.0012","finW0711")],
plag=1,
draws=100,
burnin=100,
prior="SSVS",
SV=TRUE,
eigen=1,
expert=list(variable.list=variable.list,save.shrink.store=TRUE),
trend=TRUE
)
```

Another specification would be to include a foreign variable only when
its domestic counterpart is missing. For example, when working with
nominal bilateral exchange rates we probably do not want to include also
its weighted average (which corresponds to something like an effective
exchange rate). Using the previous model we could place an exclusion
restriction on foreign long-term interest rates using
`Wex.restr`

which is again handed over via
`expert`

. The following includes foreign long-term rates only
in those country models where no domestic long-term rates are available:

```
# does include ltir* only when ltir is missing domestically
.3<-bgvar(Data=eerData,
model.ssvsW=W.trade0012,
plag=1,
draws=100,
burnin=100,
prior="SSVS",
SV=TRUE,
eigen=1,
expert=list(Wex.restr="ltir",save.shrink.store=TRUE),
trend=TRUE,
)
```

`print(model.ssvs.3) `

```
## ---------------------------------------------------------------------------
## Model Info:
## Prior: Stochastic Search Variable Selection prior (SSVS)
## Number of lags for endogenous variables: 1
## Number of lags for weakly exogenous variables: 1
## Number of posterior draws: 100/1=100
## Size of GVAR object: 0.8 Mb
## Trimming leads to 57 (57%) stable draws out of 100 total draws.
## ---------------------------------------------------------------------------
## Model specification:
##
## EA: y, Dp, rer, stir, ltir, tb, y*, Dp*, rer*, stir*, tb*, poil**, trend
## US: y, Dp, rer, stir, ltir, tb, poil, y*, Dp*, rer*, stir*, tb*, trend
## RU: y, Dp, rer, stir, tb, y*, Dp*, rer*, stir*, tb*, poil**, trend
```

Last, we could also use a different specification of oil prices in the model. Currently, the oil price is determined endogenously within the US model. Alternatively, one could set up an own standing oil price model with additional variables that feeds the oil price back into the other economies as exogenous variable (Kamiar Mohaddes and Raissi 2019).

The model structure would then look something like in the Figure below:

For that purpose we have to remove oil prices from the US model and attach them to a separate slot in the data list. This slot has to have its own country label. We use ‘OC’ for “oil country”.

```
<-eerData
eerData2$OC<-eerData$US[,c("poil"),drop=FALSE] # move oil prices into own slot
eerData2$US<-eerData$US[,c("y","Dp", "rer" , "stir", "ltir","tb")] # exclude it from US m odel eerData2
```

Now we have to specify a list object that we label
`OC.weights`

. The list has to consist of three slots with the
following names `weights`

, `variables`

and
`exo`

:

```
<-list()
OC.weights$weights<-rep(1/3, 3)
OC.weightsnames(OC.weights$weights)<-names(eerData2)[1:3] # last one is OC model, hence only until 3
$variables<-c(colnames(eerData2$OC),"y") # first entry, endog. variables, second entry weighted average of y from the other countries to proxy demand
OC.weights$exo<-"poil" OC.weights
```

The first slot, `weights`

, should be a vector of weights that
sum up to unity. In the example above, we simply use \(1/N\), other weights could include
purchasing power parities (PPP). The weights are used to aggregate
specific variables that in turn enter the oil model as weakly exogenous.
The second slot, `variables`

, should specify the names of the
endogenous and weakly exogenous variables that are used in the OC model.
In the oil price example, we include the oil price (`poil`

)
as an endogenous variable (not contained in any other country model) and
a weighted average using `weights`

of output (`y`

)
to proxy world demand as weakly exogenous variable. Next, we specify via
`exo`

which one of the endogenous variables of the OC model
are fed back into the other country models. In this example we specify
`poil`

. Last, we put all this information in a further list
called `OE.weights`

(other entity weights). This is done to
allow for multiple other entity models (i.e., an oil price model, a
joint monetary union model, etc.). It is important that the list entry
has the same name as the other entity model, in our example
`OC`

.

```
# other entities weights with same name as new oil country
<- list(OC=OC.weights) OE.weights
```

Now we can re-estimate the model where we pass on
`OE.weights`

via the `expert`

argument.

```
.4<-bgvar(Data=eerData2,
model.ssvsW=W.trade0012,
plag=1,
draws=100,
burnin=100,
prior="SSVS",
SV=TRUE,
expert=list(OE.weights=OE.weights,save.shrink.store=TRUE),
trend=TRUE
)
```

and can compare the results of the four models by e.g., looking at the average PIPs.

```
<-model.ssvs.1$cc.results$PIP$PIP.avg;aux1<-aux1[-nrow(aux1),1:6]
aux1<-model.ssvs.2$cc.results$PIP$PIP.avg;aux2<-aux2[-nrow(aux2),1:6]
aux2<-model.ssvs.3$cc.results$PIP$PIP.avg;aux3<-aux3[-nrow(aux3),1:6]
aux3<-model.ssvs.4$cc.results$PIP$PIP.avg;aux4<-aux4[-nrow(aux4),1:6] aux4
```

```
heatmap(aux1,Rowv=NA,Colv=NA, main="Model 1", cex.main=2, cex.axis=1.7)
heatmap(aux2,Rowv=NA,Colv=NA, main="Model 2", cex.main=2, cex.axis=1.7)
heatmap(aux3,Rowv=NA,Colv=NA, main="Model 3", cex.main=2, cex.axis=1.7)
heatmap(aux4,Rowv=NA,Colv=NA, main="Model 4", cex.main=2, cex.axis=1.7)
```

We could also compare the models based on their fit, the likelihood, information criteria such as the DIC, residual properties or their forecasting performance.

The package allows to calculate three different ways of dynamic responses, namely generalized impulse response functions (GIRFs) as in Pesaran and Shin (1998), orthogonalized impulse response functions using a Cholesky decomposition of the variance covariance matrix and finally impulse response functions given a set of user-specified sign restrictions.

Most of the GVAR applications deal with *locally* identified
shocks. This implies that the shock of interest is orthogonal to the
other shocks in the same unit model and hence can be interpreted in a
*structural* way. There is still correlation between the shocks
of the unit models, and these responses (the spillovers) are hence not
fully structural (Eickmeier and Ng 2015).
Hence some GVAR applications favor generalized impulse response
functions, which per se do not rely on an orthogonalization. In
`BGVAR`

, responses to both types of shocks can be easily
analyzed using the `irf`

function.

This function needs as input a model object (`x`

), the
impulse response horizon (`n.ahead`

) and the default
identification method is the recursive identification scheme via the
Cholesky decomposition. Further arguments can be passed on using the
wrapper `expert`

and are discussed in the helpfiles. The
following provides impulse response to all `N`

shocks with
unit scaling and using generalized impulse response functions:

`<-irf(model.ssvs.1, n.ahead=24, expert=list(save.store=FALSE)) irf.chol`

The results are stored in `irf.chol$posterior`

, which is a
four-dimensional array: \(K \times n.ahead
\times nr.of shocks \times Q\), with `Q`

referring to
the 50%, 68% and 95% quantiles of the posterior distribution of the
impulse response functions. The posterior median of responses to the
first shock could be accessed via
`irf.girf$posterior[,,1,"Q50"]`

Note that this example was for illustrational purposes; in most
instances, we would be interested in a particular shock and calculating
responses to all shocks in the system is rather inefficient. Hence, we
can provide the `irf`

function with more information. To be
more precise, let us assume that we are interested in an expansionary
monetary policy shock (i.e., a decrease in short-term interest rates) in
the US country model.

For that purpose, we can set up an `shockinfo`

object, which
contains information about which variable we want to shock
(`shock`

), the size of the shock (`scale`

), the
specific identification method(`ident`

), and whether it is a
shock applied in a single country or in multiple countries
(`global`

). We can use the helper function
`get_shockinfo()`

to set up a such a dummy object which we
can subsequently modify according to our needs. The following lines of
code are used for a negative 100 bp shock applied to US short term
interest rates:

```
# US monetary policy shock - Cholesky
<-get_shockinfo("chol")
shockinfo_chol$shock<-"US.stir"
shockinfo_chol$scale<--100
shockinfo_chol# US monetary policy shock - GIRF
<-get_shockinfo("girf")
shockinfo_girf$shock<-"US.stir"
shockinfo_girf$scale<--100 shockinfo_girf
```

The `shockinfo`

objects for Cholesky and GIRFs look
exactly the same but have additionally an attribute which classifies the
particular identification scheme. If we compare them, we notice that
both have three columns defining the shock, the scale and whether it is
defined as global shock. But we also see that the attributes differ
which is important for the identification in the `irf`

function.

` shockinfo_chol`

```
## shock scale global
## 1 US.stir -100 FALSE
```

` shockinfo_girf`

```
## shock scale global
## 1 US.stir -100 FALSE
```

Now, we identify a monetary policy shock with recursive identification:

`<-irf(model.ssvs.1, n.ahead=24, shockinfo=shockinfo_chol, expert=list(save.store=TRUE)) irf.chol.us.mp`

The results are stored in `irf.chol.us.mp`

. In order to
save the complete set of draws, one can activate the
`save.store`

argument by setting it to `TRUE`

within the expert settings (note: this may need a lot of storage).

`names(irf.chol.us.mp)`

```
## [1] "posterior" "ident" "shockinfo" "rot.nr" "struc.obj" "model.obj"
## [7] "IRF_store"
```

Again, `irf.chol.us.mp$posterior`

is a \(K \times n.ahead \times nr.of shocks \times
7\) object and the last slot contains the 50%, 68% and 95%
credible intervals along with the posterior median. If
`save.store=TRUE`

, `IRF_store`

contains the full
set of impulse response draws and you can calculate additional quantiles
of interest.

We can plot the complete responses of a particular country by typing:

`plot(irf.chol.us.mp, resp="US", shock="US.stir")`

The plot shows the posterior median response (solid, black line) along 50% (dark grey) and 68% (light grey) credible intervals.

We can also compare the Cholesky responses with GIRFs. For that purpose, let us look at a GDP shock.

```
# cholesky
<- get_shockinfo("chol", nr_rows = 2)
shockinfo_chol $shock <- c("US.stir","US.y")
shockinfo_chol$scale <- c(1,1)
shockinfo_chol# generalized impulse responses
<- get_shockinfo("girf", nr_rows = 2)
shockinfo_girf $shock <- c("US.stir","US.y")
shockinfo_girf$scale <- c(1,1)
shockinfo_girf# Recursive US GDP
<-irf(model.ssvs.1, n.ahead=24, shockinfo=shockinfo_chol)
irf.chol.us.y# GIRF US GDP
<-irf(model.ssvs.1, n.ahead=24, shockinfo=shockinfo_girf) irf.girf.us.y
```

```
plot(irf.chol.us.y, resp="US.y", shock="US.y")
plot(irf.girf.us.y, resp="US.y", shock="US.y")
plot(irf.chol.us.y, resp="US.rer", shock="US.y")
plot(irf.girf.us.y, resp="US.rer", shock="US.y")
```