The erfe
package estimates the expectile regression panel
fixed effect model (ERFE). The ERFE model is a expectile-based
method for panel data. The ERFE model extends the within
transformation strategy to solve the incidental parameter problem within
the expectile regression framework. The ERFE model estimates the
regressor effects on the expectiles of the response distribution. The
ERFE model captures the data heteroscedasticity and eliminates any
bias resulting from the correlation between the regressors and the
omitted factors. When
The main function of the erfe
package is the erfe
function and consists of four arguments. The predictors
matrix which corresponds to the design matrix or the matrix of
regressors. Note that, the design matrix should contain time varying
regressors only, because the ERFE model do not make inference for
time-invariant regressors. The response
variable is the
continuous response variable, and the asymp
parameter
corresponds to the vector of asymmetric points with default values:
id
parameter
corresponds to the subject ids and should be ordered according to the
time or year.
You can install the development version of the erfe
package from GitHub with:
# install.packages("devtools")
devtools::install_github("amadoudiogobarry/erfe")
This is a basic example which shows you how to use the main function of the package:
library(erfe)
data(sim_panel_data) # Toy dataset
head(sim_panel_data)
#> id pred1 pred2 resp nobs year
#> 1 1 1.9367572 2.386914 4.943895 50 1
#> 2 1 0.1368550 3.731007 4.584137 50 2
#> 3 1 5.8850632 3.600262 8.405295 50 3
#> 4 1 2.5455661 3.416180 6.011400 50 4
#> 5 1 -0.3971390 5.367943 6.237594 50 5
#> 6 2 -0.2610938 -1.326893 -3.258152 50 1
asymp <- c(0.25,0.5,0.75) # sequence of asymmetric points
predictors <- as.matrix(cbind(sim_panel_data$pred1, sim_panel_data$pred2)) # design matrix
response <- sim_panel_data$resp # response variable
id <- sim_panel_data$id # ordered subject ids variable
outlist <- erfe(predictors, response, asymp=c(0.25,0.5,0.75), id)
For each asymmetric point, we have a list of results including the
asymmetric point itself, the estimator and the estimator of its
covariance matrix, and the residuals of the model. For example, the
results of the ERFE model for
outlist75 <- outlist[[3]]
# coef estimate
outlist75$coefEst
#> X1 X2
#> 0.5995653 0.9585377
# covariance estimate
outlist75$covMat
#> 2 x 2 Matrix of class "dgeMatrix"
#> [,1] [,2]
#> [1,] 0.04042441 0.1457498
#> [2,] 0.14574977 0.6555698