Estimation and analysis of fixed effects vector autoregressive models
feVAR is an R package implementing bias-corrected least squares estimation for fixed effects vector autoregressive models (Dhaene & Jochmans, 2016), together with several post-estimation functionalities. Both panel and non-panel data are allowed.
G. Dhaene, and K. Jochmans (2016). Bias-corrected estimation of panel vector autoregressions. Economics Letters, 145: 98-103. DOI: 10.1016/j.econlet.2016.06.010
R (The R Project for Statistical Computing) needs to be installed on your system in order
to use the feVAR package. R can be downloaded from https://www.r-project.org/.
To install the feVAR package, open the console of R and type:
install.packages("devtools") ## do not run if package 'devtools' is already installed
library(devtools)
install_github("alessandromagrini/feVAR")
For any request or feedback, please write to [email protected] (Alessandro Magrini)
Below, you find some examples of use of the package.
Load the feVAR package
library(feVAR)
Load data on EU agricultural sustainability indicators
data(agrisus2020)
Estimation of a fixed effects vector autoregressive model:
# names of endogenous variables
varNames <- colnames(agrisus2020)[4:15]
# fit a model with p=1 on data in logarithmic differences ('box.cox'=0 and 'ndiff'=1)
# exogenous variable: GDP (optional)
m_agr <- feVAR(varNames, unit="Country", time="Year", exogenous="GDP",
data=agrisus2020, box.cox=0, ndiff=1, nlags=1)
# automated selection of the lag order based on BIC
m_agr_auto <- feVAR(varNames, unit="Country", time="Year", exogenous="GDP",
data=agrisus2020, box.cox=0, ndiff=1, ic="bic", max.nlags=4)
Summary of parameter estimation:
summary(m_agr)
Stability check:
eig0 <- stabilityCheck(m_agr)
which(eig0>=1) ## no eigenvalue greater or equal to 1 -> stable
Graphical diagnostics:
# time series plot of residuals
residualPlot(m_agr, type="ts", cex.main=1.1)
# autocorrelogram of residuals
residualPlot(m_agr, type="acf", cex.main=1.1)
# normal quantile plot of residuals
residualPlot(m_agr, type="qq", cex.main=1.1)
# fitted versus residuals
residualPlot(m_agr, type="fitVSres", cex.main=1.1)
Graphical functionalities for prediction:
# predictions for the first four variables on unit 'Italy'
predictPlot(m_agr, unit.id="Italy", var.names=varNames[1:4]) ## in-sample
predictPlot(m_agr, unit.id=15, var.names=1:4) ## same as before
predictPlot(m_agr, unit.id=15, var.names=1:4, n.ahead=3) ## 3 steps ahead
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