EWS_CSA_Criterion {EWS}

CSA Threshold - optimal cut-off

Description

This function provides a method to compute the optimal CSA (Credit-Scoring Approach) criterion. As defined in Candelon, Dumitrescu and Hurlin (2012), this approach consists of calculating the difference between the sensitivity and the specificity. Sensitivity represents the proportion of crisis periods correctly identified by the EWS. Specificity is the proportion of calm periods correctly identified by the EWS. The optimal cut-off minimizes the absolute value of this difference.

Usage

EWS_CSA_Criterion(Var_Proba, Dicho_Y, cutoff_interval)

Arguments

Var_Proba

Vector containing the estimated probabilities obtained with the Logistic Estimation function.

Dicho_Y

Vector of the binary time series.

cutoff_interval

Numeric variable between 0 and 1.

Value

A numeric variable containing the optimal cut-off that minimizes the absolute value of the difference between the sensitivity and the specificity.

Author(s)

Jean-Baptiste Hasse and Quentin Lajaunie

References

Basel Committee on Banking Supervision, 2005, "Studies on the Validation of Internal Rating Systems", working paper no.14, Bank for International Settlements.

Candelon, Bertrand, Elena-Ivona Dumitrescu, and Christophe Hurlin. "How to evaluate an early-warning system: Toward a unified statistical framework for assessing financial crises forecasting methods." IMF Economic Review 60.1 (2012): 75-113.

Examples

# NOT RUN {

# Import data
data("data_USA")

# Data process
Var_Y <- as.vector(data_USA$NBER)
Var_X <- as.vector(data_USA$Spread)

# Estimate the logit regression
Logistic_results <- Logistic_Estimation(Dicho_Y = Var_Y, Exp_X = Var_X, Intercept = TRUE,
                      Nb_Id = 1, Lag = 1, type_model = 4)

# Vector of probabilities
vector_proba <- as.vector(rep(0,length(Var_Y)-1))
vector_proba <- Logistic_results$prob

# Vector of binary variables
Lag <- 1
vector_binary <- as.vector(rep(0,length(Var_Y)-1))
vector_binary <- Var_Y[(1+Lag):length(Var_Y)]

# optimal cut-off that minimizes the CSA criterion
results <- EWS_CSA_Criterion(Var_Proba = vector_proba, Dicho_Y = vector_binary,
                      cutoff_interval = 0.0001)

# print results
results

#}