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bsvars_oz.R

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+
+library(bsvars)
+set.seed(123)
+
+
+
+# Download data using the readrba package
+############################################################
+
+# Gross domestic product (GDP); Chain volume
+rgdp_dwnld      = readrba::read_rba(series_id = "GGDPCVGDP")
+rgdp_tmp        = xts::xts(rgdp_dwnld$value, rgdp_dwnld$date, tclass = 'yearqtr')
+drgdp           = na.omit(400 * diff(log(rgdp_tmp)))
+drgdp           = xts::to.quarterly(drgdp, OHLC = FALSE)
+
+# Consumer price index; All groups; Quarterly change (in per cent)
+picpi_dwnld     = readrba::read_rba(series_id = "GCPIAGSAQP")
+pi              = 4 * xts::xts(picpi_dwnld$value, picpi_dwnld$date, tclass = 'yearqtr')
+pi              = xts::to.quarterly(pi, OHLC = FALSE)
+
+# Interbank Overnight Cash Rate
+cr_dwnld        = readrba::read_rba(series_id = "FIRMMCRID")   # Cash Rate Target
+cr_tmp          = xts::xts(cr_dwnld$value, cr_dwnld$date)
+cr              = xts::to.quarterly(cr_tmp, OHLC = FALSE)
+
+# Real Trade-Weighted Index
+rtwi_dwnld      = readrba::read_rba(series_id = "FRERTWI")
+rtwi_tmp        = xts::xts(rtwi_dwnld$value, rtwi_dwnld$date, tclass = 'yearqtr')
+rtwi            = 100 * na.omit(diff(log(rtwi_tmp)))
+drtwi            = xts::to.quarterly(rtwi, OHLC = FALSE)
+
+y               = na.omit(merge(drgdp, pi, cr, drtwi))
+plot(y, main = "Australian monetary system",
+     legend.loc = "bottomleft", col = c("#FF00FF","#990099","#77001b","#330033"))
+
+
+
+
+# Estimation setup
+############################################################
+N       = ncol(y)
+p       = 4
+S_burn  = 1e3
+S       = 5e3
+
+
+# estimation - lower-triangular model
+############################################################
+# specify a model
+spec = specify_bsvar$new(
+  as.matrix(y), 
+  p = p, 
+  stationary = rep(TRUE, N)
+)
+
+# estimate a model
+spec |>
+  estimate(S = S_burn) |>
+  estimate(S = S) -> post
+
+# compute and plot impulse responses
+post |> 
+  compute_impulse_responses(horizon = 20) |> 
+  plot()
+
+# compute and plot forecast error variance decompositions
+post |> 
+  compute_variance_decompositions(horizon = 20) |> 
+  plot()
+
+# compute and plot structural shocks
+post |> 
+  compute_structural_shocks() |> 
+  plot()
+
+# compute and plot fitted values
+post |> 
+  compute_fitted_values() |> 
+  plot()
+
+# compute and plot forecasts
+post |> 
+  forecast(horizon = 8, data_in_plot = 0.3) |> 
+  plot()
+