Gather data of several countries in a list. Particularly useful for GVAR-based setups (Compute "GVARFactors")

Usage

DatabasePrep(
  t_First,
  t_Last,
  Economies,
  N,
  FactorLabels,
  ModelType,
  Macro_FullData,
  Yields_FullData,
  Wgvar = NULL
)

Arguments

t_First: character. Start date of the sample period in the format yyyy-mm-dd.
t_Last: character. End date of the sample period in the format yyyy-mm-dd.
Economies: character vector. Names of the C economies included in the system.
N: positive integer. Number of country-specific spanned factors per country.
FactorLabels: list. Labels for all variables present in the model, as returned by LabFac.
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".
Macro_FullData: list. Full set of macroeconomic data, as returned by Load_Excel_Data.
Yields_FullData: list. Full set of bond yield data, as returned by Load_Excel_Data.
Wgvar: GVAR transition matrix. For GVAR models, either a matrix (C x C) for fixed weights, or a named list of matrices for time-varying weights. Default is NULL. Required for GVAR models.

Value

List containing the risk factor set for all countries and global factors. Particularly useful for GVAR-based models.

General Notation

C: number of countries in the system.
N: number of country-specific spanned factors.

Examples


# Load data from excel
macro_data <- Load_Excel_Data(system.file("extdata", "MacroData.xlsx", package = "MultiATSM"))
yields_data <- Load_Excel_Data(system.file("extdata", "YieldsData.xlsx", package = "MultiATSM"))
trade_data <- Load_Excel_Data(system.file("extdata", "TradeData.xlsx", package = "MultiATSM"))
#> New names:
#> • `` -> `...1`
#> New names:
#> • `` -> `...1`
#> New names:
#> • `` -> `...1`
#> New names:
#> • `` -> `...1`
#> New names:
#> • `` -> `...1`

# Adjust trade data
trade_data <- lapply(trade_data, function(df) {
  countries <- df[[1]]
  df <- as.data.frame(df[-1])
  rownames(df) <- countries
  df
})

# Define features of interest
ModelType <- "GVAR multi"
Economies <- c("China", "Uruguay", "Russia")
GlobalVar <- c("GBC", "CPI_OECD")
DomVar <- c("Eco_Act", "Inflation")
N <- 3
t0 <- "2006-09-01"
tF <- "2019-01-01"


# Compute some inputs
FactorLabels <- LabFac(N, DomVar, GlobalVar, Economies, ModelType)
Wgvar <- Transition_Matrix(
  t_First = "2006", t_Last = "2019", Economies,
  type = "Sample Mean", trade_data
)

# Compute GVARFactors
GVARFactors <- DatabasePrep(
  t0, tF, Economies, N, FactorLabels, ModelType, macro_data,
  yields_data, Wgvar
)