R/conn.r

In joergrieger/bayesianConnectedness: Calculates spillover measures as in Diebold-Yilmaz (2009)

#' @export
#' @title connectedness measure for classical VAR
#' @param mydata data
#' @param rolling_window size of the rolling window to estimate the VAR. If the rollingwindow is 0, the whole dataset is estimated at once.
#' @param nolags number of lags in the VAR model
#' @param nhor horizon for the forecast erro variance decomposition
#' @return an S3-object of the class spillover_table or spillover_series

cv_connectedness <- function(mydata, rolling_window = 0,nolags = 6,nhor = 12){

  # Load required Namespace for the vars packag
  chck <- requireNamespace("vars",quietly = TRUE)
  if(!chck){

    stop("Please install the vars package!")

  }

  if(rolling_window == 0){

    # Estimate the whole thing
    tmp <- vars::VAR(y=mydata, p = nolags)
    Alpha <- vars::Acoef(tmp)
    Sigma <- summary(tmp)$covres


    # Get VMA representation
    vma <- cv_var2vma(Alpha,nhor,nolags,tmp$K)


    # Get GFEVD
    fe <- gfevd(vma,Sigma,nhor)

    # Calculate spillovers from generalized gfevd
    spill_over <- array(0,dim=c(tmp$K,tmp$K))
    for(ii in 1:tmp$K){

      fe_sum <- sum(fe[ii,])
      spill_over[ii,] <- fe[ii,] / fe_sum

    }

    colnames(spill_over) <- colnames(mydata)
    rownames(spill_over) <- colnames(mydata)

    spillover <- structure(list(spillover = spill_over,
                                nvar = tmp$K,
                                nhor = nhor,
                                nolags = nolags),class="spillover_table")


  }
  else{
    # Calculate Spillovers with rolling window
    total_length <- dim(mydata)[1] - rolling_window + 1
    K <- dim(mydata)[2]
    spillover_series <- array(0,dim=c(K,K,total_length))

    for(ii in 1:total_length){

      print(ii)

      sub_series <- mydata[ii:(ii + rolling_window - 1),]

      # Estimate the sub series
      tmp <- vars::VAR(y=sub_series, p = nolags)
      Alpha <- vars::Acoef(tmp)
      Sigma <- summary(tmp)$covres

      # Get VMA representation
      vma <- cv_var2vma(Alpha,nhor,nolags,tmp$K)

      # Get GFEVD
      fe <- gfevd(vma,Sigma,nhor)

      # Calculate spillovers from generalized gfevd
      spill_over <- array(0,dim=c(tmp$K,tmp$K))
      for(ii in 1:tmp$K){

        fe_sum <- sum(fe[ii,])
        spill_over[ii,] <- fe[ii,] / fe_sum

      }
      spillover_series[,,ii] <- spill_over


    }

    spillover <- structure(list(spillover_series = spillover_series,
                                nvar = K,
                                nhor = nhor,
                                nolags = nolags),class = "spillover_series")
  }

  return(spillover)

}