EWS_NSR_Criterion {EWS}

NSR Threshold - optimal cut-off

Description

This function provides a method to compute the optimal NSR (Noise to Signal Ratio) criterion proposed by Kaminsky, Lizondo and Reinhart (1998). As defined in Candelon, Dumitrescu and Hurlin (2012), the NSR represents the ratio of the false alarms (type II error) to the number of crises correctly identified by the EWS for a given cut-off. The optimal cut-off minimizes the NSR criterion.

Usage

EWS_NSR_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 NSR criterion.

Author(s)

Jean-Baptiste Hasse and Quentin Lajaunie

References

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.

Kaminsky, Graciela, Saul Lizondo, and Carmen M. Reinhart. "Leading indicators of currency crises." IMF Staff Papers 45.1 (1998): 1-48.

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 NSR criterion
results <- EWS_NSR_Criterion(Var_Proba = vector_proba, Dicho_Y = vector_binary,
                      cutoff_interval = 0.0001)

# print results
results

#}