Merge pull request #25 from bsvars/21-truncated-normal-forecasts

21 truncated normal forecasts

R/bvarPANELs-package.R

@@ -223,7 +223,7 @@
 #' specification = specify_bvarPANEL$new(
 #'                   ilo_dynamic_panel,
 #'                   exogenous = ilo_exogenous_variables,
-#'                   type = c("real", rep("rates", 3))
+#'                   type = c("real", rep("rate", 3))
 #'                 )
 #' burn_in       = estimate(specification, S = 10)         # run the burn-in; use say S = 10000
 #' posterior     = estimate(burn_in, S = 10)               # estimate the model; use say S = 10000

R/forecast.R

@@ -52,10 +52,10 @@
 #' of these exogenous variables in the argument \code{exogenous_forecast} of the
 #' \code{\link{forecast.PosteriorBVARPANEL}} function.
 #' 
-#' \strong{Truncated forecasts for variables of type 'rates'.}
+#' \strong{Truncated forecasts for variables of type 'rate'.}
 #' The package provides the option to truncate the forecasts for variables of 
 #' for which the corresponding element of argument \code{type} of the function 
-#' \code{specify_bvarPANEL$new()} is set to \code{"rates"}. The one-period-ahead
+#' \code{specify_bvarPANEL$new()} is set to \code{"rate"}. The one-period-ahead
 #' predictive normal density for such variables is truncated to values from 
 #' interval  \eqn{[0,100]}. 
 #' 
@@ -123,7 +123,7 @@
 #' data(ilo_conditional_forecasts)                        # load the conditional forecasts of dgdp
 #' specification = specify_bvarPANEL$new(
 #'                   ilo_dynamic_panel,
-#'                   type = c("real", rep("rates", 3))
+#'                   type = c("real", rep("rate", 3))
 #'                 )   # specify the model
 #' burn_in       = estimate(specification, 10)            # run the burn-in; use say S = 10000
 #' posterior     = estimate(burn_in, 10)                  # estimate the model; use say S = 10000
@@ -134,7 +134,7 @@
 #' ############################################################
 #' set.seed(123)
 #' ilo_dynamic_panel |>
-#'   specify_bvarPANEL$new(type = c("real", rep("rates", 3))) |>
+#'   specify_bvarPANEL$new(type = c("real", rep("rate", 3))) |>
 #'   estimate(S = 10) |> 
 #'   estimate(S = 20) |> 
 #'   forecast(
@@ -205,7 +205,7 @@ forecast.PosteriorBVARPANEL = function(
   type      = posterior$last_draw$data_matrices$type
   LB        = rep(-Inf, N)
   UB        = rep(Inf, N)
-  rates_id  = which(type == "rates")
+  rates_id  = which(type == "rate")
   if (length(rates_id) > 0) {
     LB[rates_id] = 0
     UB[rates_id] = 100

R/specify_bvarpanel.R

@@ -305,7 +305,7 @@ specify_panel_data_matrices = R6::R6Class(
     #' regressors, \eqn{\mathbf{X}_c}. 
     X     = list(),
 
-    #' @field type an \code{N} character vector with elements set to "rates" or "real"
+    #' @field type an \code{N} character vector with elements set to "rate" or "real"
     #' determining the truncation of the predictive density to \code{[0, 100]} and
     #' \code{(-Inf, Inf)} (no truncation) for each of the variables.
     type  = character(),
@@ -318,7 +318,7 @@ specify_panel_data_matrices = R6::R6Class(
     #' @param exogenous a list containing \code{(T_c+p)xd} matrices with 
     #' country-specific of exogenous variables. This matrix should not include a 
     #' constant term.
-    #' @param type an \code{N} character vector with elements set to "rates" or "real"
+    #' @param type an \code{N} character vector with elements set to "rate" or "real"
     #' determining the truncation of the predictive density to \code{[0, 100]} and
     #' \code{(-Inf, Inf)} (no truncation) for each of the variables.
     #' @return New data matrices DataMatricesBVARPANEL
@@ -332,8 +332,8 @@ specify_panel_data_matrices = R6::R6Class(
       }
       stopifnot("Argument p must be a positive integer number." = p > 0 & p %% 1 == 0)
 
-      stopifnot("Argument type must include elements 'rates' or 'real'." 
-                = all(unique(type) %in% c("rates", "real")) )
+      stopifnot("Argument type must include elements 'rate' or 'real'." 
+                = all(unique(type) %in% c("rate", "real")) )
       stopifnot("Argument type must be of length corresponding to the numbers of variables."
                 = length(type) == ncol(data[[1]]) )
 
@@ -443,7 +443,7 @@ specify_bvarPANEL = R6::R6Class(
     #' @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.
-    #' @param type an \code{N} character vector with elements set to "rates" or "real"
+    #' @param type an \code{N} character vector with elements set to "rate" or "real"
     #' determining the truncation of the predictive density to \code{[0, 100]} and
     #' \code{(-Inf, Inf)} (no truncation) for each of the variables.
     #' @return A new complete specification for the Bayesian Panel VAR model BVARPANEL.

inst/tinytest/test_forecast.R

@@ -141,20 +141,20 @@ expect_error(
 # truncated forecasts
 
 expect_identical(
-  class(specify_bvarPANEL$new(ilo_dynamic_panel, type = c("real",rep("rates",3))))[1], 
+  class(specify_bvarPANEL$new(ilo_dynamic_panel, type = c("real",rep("rate",3))))[1], 
   "BVARPANEL",
   info = "truncated forecast: good specification of argument type."
 )
 
 expect_error(
-  specify_bvarPANEL$new(ilo_dynamic_panel, type = rep("rates",3)),
+  specify_bvarPANEL$new(ilo_dynamic_panel, type = rep("rate",3)),
   pattern = "length",
   info = "truncated forecast: wrong specification of argument type."
 )
 
 set.seed(1)
 suppressMessages(
-  specification_no1 <- specify_bvarPANEL$new(ilo_dynamic_panel, type = c("real",rep("rates",3)))
+  specification_no1 <- specify_bvarPANEL$new(ilo_dynamic_panel, type = c("real",rep("rate",3)))
 )
 run_no1             <- estimate(specification_no1, 3, 1, show_progress = FALSE)
 suppressMessages(