R/print.summary.ctryvarest.R

In franzmohr/bgvars: Bayesian Inference of Global Vector Autoregressive and Gloal Vector Error Correction Models

#' @include summary.ctryvarest.R
#'
#' @export
#' @rdname summary.ctryvarest
print.summary.ctryvarest <- function(x, digits = max(3L, getOption("digits") - 3L), ...){
  
  # Title
  title_text <- "\nBayesian " 
  tvp <- x[["model"]][["tvp"]]
  if (tvp) {
    title_text <- paste0(title_text, "TVP-")
  }
  if (x[["model"]][["sv"]]) {
    title_text <- paste0(title_text, "SV-")
  }
  if (x[["model"]][["structural"]]) {
    title_text <- paste0(title_text, "S")
  }
  title_text <- paste0(title_text, "VARX submodel")
  
  cat(title_text, "\n")
  
  # Model
  
  if (is.null(x[["coefficients"]][["means"]])) {
    regressors <- "nothing"
    use_a <- FALSE
  } else {
    regressors <- paste(dimnames(x[["coefficients"]][["means"]])[[2]], collapse = " + ")
    use_a <- TRUE
  }
  
  cat("\nModel:\n\n",
      paste("y ~ ", regressors,
            sep = ""), "\n", sep = "")
  
  if (!is.null(x[["model"]][["period"]])) {
    cat("\nPeriod:", x[["model"]][["period"]], "\n")
  }
  
  k <- length(x[["model"]][["domestic"]][["variables"]])
  y_names <- x[["model"]][["domestic"]][["variables"]]
  
  # Coefficients per endogenous variable
  
  if (use_a) {
    for (i in 1:k) {
      temp <- cbind(x[["coefficients"]][["means"]][i, ],
                    x[["coefficients"]][["sd"]][i, ],
                    x[["coefficients"]][["naivesd"]][i, ],
                    x[["coefficients"]][["tssd"]][i, ],
                    x[["coefficients"]][["q_lower"]][i, ],
                    x[["coefficients"]][["median"]][i, ],
                    x[["coefficients"]][["q_upper"]][i, ])
      
      dim_names_1 <- dimnames(x[["coefficients"]][["means"]])[[2]]
      dim_names_2 <- c("Mean", "SD", "Naive SD", "Time-series SD",
                       x[["model"]][["ci"]][1], "50%", x[["model"]][["ci"]][2])
      
      if ("lambda" %in% names(x[["coefficients"]])) {
        temp <- cbind(temp, x[["coefficients"]][["lambda"]][i, ])
        dim_names_2 <- c(dim_names_2, "Incl. prob.")
      }
      
      dimnames(temp)[[1]] <- dim_names_1
      dimnames(temp)[[2]] <- dim_names_2
      
      cat("\nVariable:", y_names[i], "\n\n")
      print.default(temp, quote = FALSE, right = TRUE, digits = digits)
    } 
  } else {
    cat("\n\nNo regressors.\n\n")
  }
  
  # Error covariance matrix
  
  if (!is.null(x[["sigma"]])) {
    x_names <- NULL
    for (i in 1:k) {
      x_names <- c(x_names , paste(dimnames(x[["sigma"]][["means"]])[[1]][i], dimnames(x[["sigma"]][["means"]])[[1]], sep = "_"))
    }
    
    temp <- cbind(matrix(x[["sigma"]][["means"]]),
                  matrix(x[["sigma"]][["sd"]]),
                  matrix(x[["sigma"]][["naivesd"]]),
                  matrix(x[["sigma"]][["tssd"]]),
                  matrix(x[["sigma"]][["q_lower"]]),
                  matrix(x[["sigma"]][["median"]]),
                  matrix(x[["sigma"]][["q_upper"]]))
    
    dim_names_1 <- x_names
    dim_names_2 <- c("Mean", "SD", "Naive SD", "Time-series SD",
                     x[["model"]][["ci"]][1], "50%", x[["model"]][["ci"]][2])
    
    if ("lambda" %in% names(x[["sigma"]])) {
      temp <- cbind(temp, matrix(x[["sigma"]][["lambda"]]))
      dim_names_2 <- c(dim_names_2, "Incl. prob.")
    }
    
    dimnames(temp) <- list(dim_names_1, 
                           dim_names_2)
    
    if (k == 1) {
      cat("\nVariance:\n\n")
    } else {
      cat("\nVariance-covariance matrix:\n\n") 
    }
    print.default(temp, quote = FALSE, right = TRUE, digits = digits)
  }
  
  cat("\n")
  invisible(x)
}