Description Usage Arguments Details Value Author(s) Examples
This model implements a forecasting method using multinomial logistic regression (also known as Softmax Regression in machine learning parlance).
1 2 | transForecast_mnl(data, histData, predData_mnl, startDate, endDate, method,
interval,snapshots, defind,ref, depVar, indVars, ratingCat, wgt)
|
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_mnl |
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 |
ref |
base or reference category for the dependent variable. |
depVar |
dependent variable, as a string. |
indVars |
list containing the independent variables |
ratingCat |
list containing the unique rating categories |
wgt |
weights |
Multinomial logistic regression is a simple extension of binary logistic regression that allows for more than two categories of the dependent or outcome variable. Whereas, a binary logistic regression model compares one dichotomy, the multinomial logistic regression model compares a number of dichotomies. Like binary logistic regression, multinomial logistic regression uses maximum likelihood estimation to evaluate the probability of categorical membership.
Assume there are 1,2,3 ...K groups in a dataset, and group 1 is the one chosen as the reference category. The logistic model states that the probability of falling into group j given the set of predictor values x is given by the general expression
P(y=k|\emph{X}) = \frac{exp(\emph{X}β_{k})}{1+∑_{j=2}^{N}exp(\emph{X}β_{j})}
The output consists of a forecasted transition matrix.
Abdoulaye (Ab) N'Diaye
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ## Not run:
library(dplyr)
library(plyr)
library(Matrix)
for (i in c(24, 25, 26)) {
data <- data
attach(data)
data2 <- data[order(ID, Date),]
detach(data)
data <- data2
rm(data2)
histData <- histData
predData_mnl2 <- predData_mnl_Baseline
predData_mnl2 <- subset(
predData_mnl,
X == i,
select = c(Market.Volatility.Index..Level.,
D_B,
D_C,
D_D,
D_E,
D_F,
D_G
)
)
indVars = c("Market.Volatility.Index..Level."
)
startDate = "1991-08-16"
endDate = "2007-08-16"
method = "cohort"
snapshots = 1
interval = 1
ref = 'A'
depVar = c("end_rating")
ratingCat = c("A", "B", "C", "D", "E", "F", "G", "N")
defind = "N"
wgt = "mCount"
transForecast_mnl_out <-
transForecast_mnl(
data,
histData,
predData_mnl2,
startDate,
endDate,
method,
interval,
snapshots,
defind,
ref,
depVar,
indVars,
ratingCat,
wgt
)
output <- transForecast_mnl_out$mnl_Predict
print(output)
}
## End(Not run)
|
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