Vector_Error {EWS}

Vector of Errors

Description

The function measures the estimation errors from the logistic estimation, and stores them in a vector. This function is used to initialize a shock in impulse response analysis as in Koop, Pesaran and Potter (1996).

Usage

Vector_Error(Dicho_Y, Exp_X, Intercept, Nb_Id, Lag, type_model)

Arguments

Dicho_Y

Vector of the binary time series.

Exp_X

Vector or Matrix of explanatory time series.

Intercept

Boolean value: TRUE for an estimation with intercept, and FALSE otherwise.

Nb_Id

Number of individuals studied for a panel approach. Nb_Id=1 in the univariate case.

Lag

Number of lags used for the estimation.

type_model

Model number: 1, 2, 3 or 4.

Value

A numeric vector containing estimation errors.

Author(s)

Jean-Baptiste Hasse and Quentin Lajaunie

References

Kauppi, Heikki, and Pentti Saikkonen. "Predicting US recessions with dynamic binary response models." The Review of Economics and Statistics 90.4 (2008): 777-791.

Koop, Gary, M. Hashem Pesaran, and Simon M. Potter. "Impulse response analysis in nonlinear multivariate models." Journal of econometrics 74.1 (1996): 119-147.

Lajaunie, Quentin. Generalized Impulse Response Function for Dichotomous Models. No. 2852. Orleans Economics Laboratory/Laboratoire d'Economie d'Orleans (LEO), University of Orleans, 2021.

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 estimation errors
results <- Vector_Error(Dicho_Y = Var_Y, Exp_X = Var_X, Intercept = TRUE,
                      Nb_Id = 1, Lag = 1, type_model = 4)

# print results
results

#}