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Merge pull request #28 from bsvars/23-update-the-forecast-method
23 update the forecast method
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| #' @title Forecasting using Structural Vector Autoregression | ||
| #' | ||
| #' @description Samples from the joint predictive density of all of the dependent | ||
| #' variables for models from packages \pkg{bsvars}, \pkg{bsvarSIGNs} or | ||
| #' \pkg{bvarPANELs} at forecast horizons from 1 to \code{horizon} specified as | ||
| #' an argument of the function. | ||
| #' | ||
| #' @method forecast PosteriorBSVARSIGN | ||
| #' @param posterior posterior estimation outcome - an object of class | ||
| #' \code{PosteriorBSVARSIGN} obtained by running the \code{estimate} function. | ||
| #' @param horizon a positive integer, specifying the forecasting horizon. | ||
| #' @param exogenous_forecast a matrix of dimension \code{horizon x d} containing | ||
| #' forecasted values of the exogenous variables. | ||
| #' @param conditional_forecast a \code{horizon x N} matrix with forecasted values | ||
| #' for selected variables. It should only contain \code{numeric} or \code{NA} | ||
| #' values. The entries with \code{NA} values correspond to the values that are | ||
| #' forecasted conditionally on the realisations provided as \code{numeric} values. | ||
| #' | ||
| #' @return A list of class \code{Forecasts} containing the | ||
| #' draws from the predictive density and data. The output list includes element: | ||
| #' | ||
| #' \describe{ | ||
| #' \item{forecasts}{an \code{NxhorizonxS} array with the draws from predictive density} | ||
| #' \item{Y}{an \eqn{NxT} matrix with the data on dependent variables} | ||
| #' } | ||
| #' | ||
| #' @author Tomasz Woźniak \email{[email protected]} and Xiaolei Wang \email{[email protected]} | ||
| #' | ||
| #' @examples | ||
| #' # upload data | ||
| #' data(oil) | ||
| #' | ||
| #' # specify the model and set seed | ||
| #' set.seed(123) | ||
| #' sign_irf = array(matrix(c(-1, -1, 1, rep(0, 6)), nrow = 3), dim = c(3, 3, 1)) | ||
| #' specification = specify_bsvarSIGN$new(oil, p = 12, sign_irf = sign_irf) | ||
| #' | ||
| #' # estimate the model | ||
| #' posterior = estimate(specification, 20) | ||
| #' | ||
| #' # sample from predictive density 1 year ahead | ||
| #' predictive = forecast(posterior, 4) | ||
| #' | ||
| #' # workflow with the pipe |> | ||
| #' ############################################################ | ||
| #' set.seed(123) | ||
| #' oil |> | ||
| #' specify_bsvarSIGN$new(p = 12, sign_irf = sign_irf) |> | ||
| #' estimate(S = 20) |> | ||
| #' forecast(horizon = 4) -> predictive | ||
| #' | ||
| #' # conditional forecasting 2 quarters ahead conditioning on | ||
| #' # provided future values for the Gross Domestic Product | ||
| #' ############################################################ | ||
| #' cf = matrix(NA , 2, 3) | ||
| #' cf[,3] = tail(oil, 1)[3] # conditional forecasts equal to the last gdp observation | ||
| #' predictive = forecast(posterior, 2, conditional_forecast = cf) | ||
| #' | ||
| #' # workflow with the pipe |> | ||
| #' ############################################################ | ||
| #' set.seed(123) | ||
| #' oil |> | ||
| #' specify_bsvarSIGN$new(p = 12, sign_irf = sign_irf) |> | ||
| #' estimate(S = 10) |> | ||
| #' forecast(horizon = 2, conditional_forecast = cf) -> predictive | ||
| #' | ||
| #' @export | ||
| forecast.PosteriorBSVARSIGN = function( | ||
| posterior, | ||
| horizon = 1, | ||
| exogenous_forecast = NULL, | ||
| conditional_forecast = NULL | ||
| ) { | ||
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| posterior_Sigma = posterior$posterior$Sigma | ||
| posterior_A = posterior$posterior$A | ||
| T = nrow(posterior$last_draw$data_matrices$X) | ||
| X_T = posterior$last_draw$data_matrices$X[T,] | ||
| Y = posterior$last_draw$data_matrices$Y | ||
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| N = nrow(posterior_Sigma) | ||
| K = length(X_T) | ||
| d = K - N * posterior$last_draw$p - 1 | ||
| S = dim(posterior_Sigma)[3] | ||
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| # prepare forecasting with exogenous variables | ||
| if (d == 0 ) { | ||
| exogenous_forecast = matrix(NA, horizon, 1) | ||
| } else { | ||
| stopifnot("Forecasted values of exogenous variables are missing." = (d > 0) & !is.null(exogenous_forecast)) | ||
| stopifnot("The matrix of exogenous_forecast does not have a correct number of columns." = ncol(exogenous_forecast) == d) | ||
| stopifnot("Provide exogenous_forecast for all forecast periods specified by argument horizon." = nrow(exogenous_forecast) == horizon) | ||
| stopifnot("Argument exogenous has to be a matrix." = is.matrix(exogenous_forecast) & is.numeric(exogenous_forecast)) | ||
| stopifnot("Argument exogenous cannot include missing values." = sum(is.na(exogenous_forecast)) == 0 ) | ||
| } | ||
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| # prepare forecasting with conditional forecasts | ||
| if ( is.null(conditional_forecast) ) { | ||
| # this will not be used for forecasting, but needs to be provided | ||
| conditional_forecast = matrix(NA, horizon, N) | ||
| } else { | ||
| stopifnot("Argument conditional_forecast must be a matrix with numeric values." | ||
| = is.matrix(conditional_forecast) & is.numeric(conditional_forecast) | ||
| ) | ||
| stopifnot("Argument conditional_forecast must have the number of rows equal to | ||
| the value of argument horizon." | ||
| = nrow(conditional_forecast) == horizon | ||
| ) | ||
| stopifnot("Argument conditional_forecast must have the number of columns | ||
| equal to the number of columns in the used data." | ||
| = ncol(conditional_forecast) == N | ||
| ) | ||
| } | ||
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| # perform forecasting | ||
| for_y = .Call(`_bsvarSIGNs_forecast_bsvarSIGNs`, | ||
| posterior_Sigma, | ||
| posterior_A, | ||
| X_T, | ||
| exogenous_forecast, | ||
| conditional_forecast, | ||
| horizon | ||
| ) # END .Call | ||
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| fore = list() | ||
| fore$forecasts = for_y | ||
| fore$Y = Y | ||
| class(fore) = "Forecasts" | ||
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| return(fore) | ||
| } # END forecast.PosteriorBSVARSIGN |
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| data(oil) | ||
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| # for bsvar | ||
| set.seed(1) | ||
| suppressMessages( | ||
| specification_no1 <- specify_bsvarSIGN$new(oil, p = 1) | ||
| ) | ||
| run_no1 <- estimate(specification_no1, 3, 1, show_progress = FALSE) | ||
| ff <- forecast(run_no1, horizon = 2) | ||
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| set.seed(1) | ||
| suppressMessages( | ||
| ff2 <- oil |> | ||
| specify_bsvarSIGN$new(p = 1) |> | ||
| estimate(S = 3, thin = 1, show_progress = FALSE) |> | ||
| forecast(horizon = 2) | ||
| ) | ||
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| expect_identical( | ||
| ff$forecasts[1,1,1], ff2$forecasts[1,1,1], | ||
| info = "forecast: forecast identical for normal and pipe workflow." | ||
| ) | ||
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| expect_true( | ||
| is.numeric(ff$forecasts) & is.array(ff$forecasts), | ||
| info = "forecast: returns numeric array." | ||
| ) | ||
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| expect_error( | ||
| specify_bsvar$new(us_fiscal_lsuw) |> forecast(horizon = 3), | ||
| info = "forecast: wrong input provided." | ||
| ) | ||
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| expect_error( | ||
| forecast(run_no1, horizon = 1.5), | ||
| info = "forecast: specify horizon as integer." | ||
| ) | ||
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