RTransProb (version 0.3.3)

transForecast_lda: Forecast - using Linear Discriminant Analysis

Description

This model implements a forecasting method using Linear Discriminant Analysis.

Usage

transForecast_lda(data, histData, predData_lda, startDate, endDate, method,
                         interval, snapshots, defind, depVar, indVars, pct, ratingCat)

Arguments

data

a table containing historical credit ratings data (i.e., credit migration data). A dataframe of size nRecords x 3 where each row contains an ID (column 1), a date (column 2), and a credit rating (column 3); The credit rating is the rating assigned to the corresponding ID on the corresponding date.

histData

historical macroeconomic,financial and non-financial data.

predData_lda

forecasting data.

startDate

start date of the estimation time window, in string or numeric format.

endDate

end date of the estimation time window, in string or numeric format.

method

estimation algorithm, in string format. Valid values are 'duration' or 'cohort'.

interval

the length of the transition interval under consideration, in years. The default value is 1, i.e., 1-year transition probabilities are estimated.

snapshots

integer indicating the number of credit-rating snapshots per year to be considered for the estimation. Valid values are 1, 4, or 12. The default value is 1, i.e., one snapshot per year. This parameter is only used in the 'cohort' algorithm.

defind

Default Indicator

depVar

dependent variable, as a string.

indVars

list containing the independent variables.

pct

percent of data used in training dataset.

ratingCat

list containing the unique rating caetgories

Value

The output consists of a forecasted transition matrix.

Examples

# NOT RUN {
library(dplyr)
library(plyr)
library(Matrix)


for (i in c(24, 25, 26)) {
  data <- data
  
  histData <- histData.normz
  
  
  predData_lda2 <- predData_lda_Baseline
  predData_lda2 <- subset(
    predData_lda2,
    X == i,
    select = c(Market.Volatility.Index..Level..normz

    )
  )
  
  indVars   = c("Market.Volatility.Index..Level..normz"

  )
  
  
  
  startDate = "1991-08-16"
  endDate   = "2007-08-16"
  
  
  depVar <- c("end_rating")
  pct <- 1
  wgt <-  "mCount"
  ratingCat <- c("A", "B", "C", "D", "E", "F", "G")
  defind    <- "G"
  method    = "cohort"
  snapshots = 1
  interval  = 1
  
  
  lda_TM <-
    transForecast_lda(
      data,
      histData,
      predData_lda2,
      startDate,
      endDate,
      method,
      interval,
      snapshots,
      defind,
      depVar,
      indVars,
      pct,
      ratingCat
    )
  print(lda_TM)
  
}

# }