binHeckman: Simulated data

In EOgundimu300/HeckmanSelect: Regularization method for binary Heckman selection model

Description

There are 1000 observations and 12 predictors. beta <- c(-2.78,0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0.7, 0.7, 0.7) gamma <- c(1.9,0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0.7, 0.7, 0.7, 1). Variable x12 is used for exclusion restriction in the selection equation

Usage

data(binHeckman)

Format

A data frame with 1000 rows and 15 variables

yobs

observed outcome variable

uu

selection indicator

ystar

underlying outcome variable. It is not needed for the modelling

x1-x12

predictor variables

References

Ogundimu EO (2022) On Lasso and Adaptive Lasso for non-random sample in credit scoring

Examples

data(binHeckman)


selection <- uu~ X1+ X2 + X3+ X4+ X5+ X6+ X7+ X8 + X9 + X10 +X11+X12
outcome <- yobs ~ X1+ X2 + X3+ X4+ X5+ X6+ X7+ X8 + X9 + X10 +X11
pp <- HeckSelect(selection, outcome, data=binHeckman, allowParallel = TRUE, penalty="ALASSO", Model="AMH",crit="bic")

EOgundimu300/HeckmanSelect documentation built on Feb. 5, 2022, 2:48 a.m.