R/dfm-methods.R

In SebKrantz/dynfacto_R: Dynamic Factor Model Estimation for Nowcasting

#' Summary information on dynamic factor model estimation
#'
#' @param x An object of \code{dfm} class
#' @param plot If \code{TRUE}, returns a bunch of summary plots
#' @return Prints out a summary information following a dynamic factor
#' model estimation. Also can return summary plots.
#' @importFrom graphics boxplot
#' @importFrom stats cov
summary.dfm <- function(object, ...) {

  nf <- dim(x$qml)[2]

  cat("Observation equation matrix: \n")
  print(x$C)

  cat("\nObservation residual covariance matrix: \n")
  print(cov(x$data - t(x$C %*% t(x$qml))))

  cat("\nSystem equation transition matrix: \n")
  print(x$A)

}

#' Print summary
#'
#' @param x Summary of \code{dfm} object
#' @export
print.summary.dfm <- function(x, ...) {
  x
}


#' Plot dfm
#' @param object Object
#' @export
plot.dfm <- function(object, ...) {

  par(mfrow=c((nf+1),1))
  for (i in 1:nf) {
    plot.title <- paste0("QML estimated factor ", i)
    plot(x$qml[,i], type='l', main=plot.title, ylab="Value", xlab="Time")
  }
  boxplot(x$data - t(x$C %*% t(x$qml)), main="Residuals by input variable")
}


#' Predict factors and observables based on an estimated dynamic
#' factor model
#'
#' @param x An object of \code{dfm} class
#' @param h Forecasting horizon
#' @return Returns prediction matrices for factors and input variables
#' based on model estimation. Each row corresponds to a prediction
#' horizon. First row is \code{T+1}, second -\code{T+2} and so on...
predict.dfm <- function(x, h=1) {

  cl <- match.call()

  nf <- dim(x$qml)[2]
  ny <- dim(x$C)[1]

  factor_forecast <- matrix(NA, nrow=h, ncol=nf)
  y_forecast <- matrix(NA, nrow=h, ncol=ny)
  factor_last <- matrix(NA, nrow=x$p, ncol=nf)

  factor_last <- tail(x$qml, x$p)

  for (i in 1:h) {

    reg_factor <- tail(factor_last, x$p)
    factor_forecast[i,] <- x$A %*% matrix(t(reg_factor[rev(1:nrow(reg_factor)),]))
    y_forecast[i,] <- x$C %*% factor_forecast[i,]

    factor_last <- rbind(factor_last, factor_forecast[i,])
  }

  return(list("y" = y_forecast,
              "f" = factor_forecast))
}