diff --git a/R/specify_bvarpanel.R b/R/specify_bvarpanel.R
index aee1e83..6142408 100644
--- a/R/specify_bvarpanel.R
+++ b/R/specify_bvarpanel.R
@@ -77,20 +77,24 @@ specify_prior_bvarPANEL = R6::R6Class(
     #' @param N a positive integer - the number of dependent variables in the model.
     #' @param p a positive integer - the autoregressive lag order of the SVAR model.
     #' @param d a positive integer - the number of \code{exogenous} variables in the model.
+    #' @param stationary an \code{N} logical vector - its element set to 
+    #' \code{FALSE} sets the prior mean for the autoregressive parameters of the 
+    #' \code{N}th equation to the white noise process, otherwise to random walk.
     #' @return A new prior specification PriorBVARPANEL.
     #' @examples 
     #' # a prior for 2-country, 3-variable example with one lag and stationary data
     #' prior = specify_prior_bvarPANEL$new(C = 2, N = 3, p = 1)
     #' prior$M
     #' 
-    initialize = function(C, N, p, d = 0){
+    initialize = function(C, N, p, d = 0, stationary = rep(FALSE, N)) {
       stopifnot("Argument C must be a positive integer number." = C > 0 & C %% 1 == 0)
       stopifnot("Argument N must be a positive integer number." = N > 0 & N %% 1 == 0)
       stopifnot("Argument p must be a positive integer number." = p > 0 & p %% 1 == 0)
       stopifnot("Argument d must be a non-negative integer number." = d >= 0 & d %% 1 == 0)
+      stopifnot("Argument stationary must be a logical vector of length N." = length(stationary) == N & is.logical(stationary))
     
       K                 = N * p + 1 + d
-      self$M            = t(cbind(diag(N), matrix(0, N, K - N)))
+      self$M            = t(cbind(diag(as.numeric(!stationary)), matrix(0, N, K - N)))
       self$W            = diag(c(kronecker((1:p)^2, rep(1, N) ), rep(10, 1 + d)))
       self$S_inv        = diag(N)
       self$S_Sigma_inv  = diag(N)
@@ -421,11 +425,15 @@ specify_bvarPANEL = R6::R6Class(
     #' with time series data.
     #' @param p a positive integer providing model's autoregressive lag order.
     #' @param exogenous a \code{(T+p)xd} matrix of exogenous variables. 
+    #' @param stationary an \code{N} logical vector - its element set to 
+    #' \code{FALSE} sets the prior mean for the autoregressive parameters of the 
+    #' \code{N}th equation to the white noise process, otherwise to random walk.
     #' @return A new complete specification for the Bayesian Panel VAR model BVARPANEL.
     initialize = function(
     data,
     p = 1L,
-    exogenous = NULL
+    exogenous = NULL,
+    stationary = rep(FALSE, ncol(data[[1]]))
     ) {
       stopifnot("Argument data has to contain matrices with the same number of columns." = length(unique(simplify2array(lapply(data, ncol)))) == 1)
       stopifnot("Argument p has to be a positive integer." = ((p %% 1) == 0 & p > 0))
@@ -439,7 +447,7 @@ specify_bvarPANEL = R6::R6Class(
       }
       
       self$data_matrices   = specify_panel_data_matrices$new(data, self$p, exogenous)
-      self$prior           = specify_prior_bvarPANEL$new(C, N, self$p, d)
+      self$prior           = specify_prior_bvarPANEL$new(C, N, self$p, d, stationary)
       self$starting_values = specify_starting_values_bvarPANEL$new(C, N, self$p, d)
       self$adaptiveMH      = c(0.44, 0.6)
     }, # END initialize
diff --git a/man/specify_bvarPANEL.Rd b/man/specify_bvarPANEL.Rd
index b3d0a7a..a4da62c 100644
--- a/man/specify_bvarPANEL.Rd
+++ b/man/specify_bvarPANEL.Rd
@@ -97,7 +97,12 @@ start adapting, the initial scaling rate}
 \subsection{Method \code{new()}}{
 Create a new specification of the Bayesian Panel VAR model BVARPANEL.
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{specify_bvarPANEL$new(data, p = 1L, exogenous = NULL)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{specify_bvarPANEL$new(
+  data,
+  p = 1L,
+  exogenous = NULL,
+  stationary = rep(FALSE, ncol(data[[1]]))
+)}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
@@ -109,6 +114,10 @@ with time series data.}
 \item{\code{p}}{a positive integer providing model's autoregressive lag order.}
 
 \item{\code{exogenous}}{a \code{(T+p)xd} matrix of exogenous variables.}
+
+\item{\code{stationary}}{an \code{N} logical vector - its element set to 
+\code{FALSE} sets the prior mean for the autoregressive parameters of the 
+\code{N}th equation to the white noise process, otherwise to random walk.}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/specify_prior_bvarPANEL.Rd b/man/specify_prior_bvarPANEL.Rd
index aa743f8..aa50a49 100644
--- a/man/specify_prior_bvarPANEL.Rd
+++ b/man/specify_prior_bvarPANEL.Rd
@@ -92,7 +92,7 @@ inverted-gamma 2 prior distribution for parameter \eqn{s}.}
 \subsection{Method \code{new()}}{
 Create a new prior specification PriorBVARPANEL.
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{specify_prior_bvarPANEL$new(C, N, p, d = 0)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{specify_prior_bvarPANEL$new(C, N, p, d = 0, stationary = rep(FALSE, N))}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
@@ -105,6 +105,10 @@ Create a new prior specification PriorBVARPANEL.
 \item{\code{p}}{a positive integer - the autoregressive lag order of the SVAR model.}
 
 \item{\code{d}}{a positive integer - the number of \code{exogenous} variables in the model.}
+
+\item{\code{stationary}}{an \code{N} logical vector - its element set to 
+\code{FALSE} sets the prior mean for the autoregressive parameters of the 
+\code{N}th equation to the white noise process, otherwise to random walk.}
 }
 \if{html}{\out{</div>}}
 }