Generates forecasts of bond yields for all model types

Generates forecasts of bond yields for all model types

Usage

ForecastYields(
  ModelType,
  ModelPara,
  InputsForOutputs,
  FactorLabels,
  Economies,
  JLLlist = NULL,
  GVARlist = NULL,
  WishBRW = FALSE,
  BRWlist = NULL,
  Folder2save = NULL,
  verbose = TRUE
)

Arguments

ModelType

character. Model type to be estimated. Permissible choices: "JPS original", "JPS global", "GVAR single", "JPS multi", "GVAR multi", "JLL original", "JLL No DomUnit", "JLL joint Sigma".

ModelPara

list. Point estimates of the model parameters. See outputs from Optimization.

InputsForOutputs

list. Inputs for generating IRFs, GIRFs, FEVDs, GFEVDs, and Term Premia.

FactorLabels

list. Labels for all variables present in the model, as returned by LabFac.

Economies

character vector. Names of the C economies included in the system.

JLLlist

list. Inputs for JLL model estimation (see JLL). Default is NULL.

GVARlist

list. Inputs for GVAR model estimation (see GVAR). Default is NULL.

WishBRW

logical. Whether to estimate the physical parameter model with bias correction (see Bias_Correc_VAR). Default is FALSE.

BRWlist

list. Inputs for bias-corrected estimation (see Bias_Correc_VAR).

Folder2save

character. Folder path where outputs will be stored. Default saves outputs in a temporary directory.

verbose

logical. Print progress messages. Default is TRUE.

Value

An object of class 'ATSMModelForecast' containing the following elements:

1. Out-of-sample forecasts of bond yields per forecast horizon

2. Out-of-sample forecast errors of bond yields per forecast horizon

3. Root mean square errors per forecast horizon

Permissible options - forecast list (InputsForOutputs input)

• ForHoriz: forecast horizon. Must be a positive integer.

• t0Sample: initial sample date. Must be a positive integer smaller than the time series dimension of the model (Td)

• t0Forecast: last sample date for the first forecast. Note that Td > t0Forecast + ForHoriz.

• ForType: "Rolling" (rolling window forecast) or "Expanding" (for expanding window forecast)

Available Methods

- `plot(object)`

Examples

# \donttest{
data("ParaSetEx")
data("InpForOutEx")
# Adjust inputs according to the loaded features
ModelType <- "JPS original"
Economy <- "Brazil"
FacLab <- LabFac(N = 1, DomVar = "Eco_Act", GlobalVar = "Gl_Eco_Act", Economy, ModelType)
 # Adjust Forecasting setting
InpForOutEx[[ModelType]]$Forecasting <- list(
  WishForecast = 1, ForHoriz = 12, t0Sample = 1,
  t0Forecast = 143, ForType = "Expanding"
)

Forecast <- ForecastYields(ModelType, ParaSetEx, InpForOutEx, FacLab, Economy,
  WishBRW = FALSE, verbose = TRUE
)
#> 4) OUT-OF-SAMPLE FORECASTING ANALYSIS
#> Out-of-sample forecast for the information set: 01-07-2006 || 01-05-2018 
#> Out-of-sample forecast for the information set: 01-07-2006 || 01-06-2018 
#> Out-of-sample forecast for the information set: 01-07-2006 || 01-07-2018 
#> Elapsed time: 3.39 seconds
# }