normexp {dsfa}R Documentation

normexp family

Description

The normexp family implements the normal-exponential distribution in which the \mu, \sigma_V and \lambda can depend on additive predictors. Useable only with gam(), the additive predictors are specified via a list of formulae.

Usage

normexp(link = list("identity", "log", "log"), s = -1)

Arguments

link

three item list specifying the link for the \mu, \sigma_V and \lambda parameters. See details.

s

s=-1 for production and s=1 for cost function.

Details

Used with gam to fit distributional stochastic frontier model. The function gam() is from the mgcv package is called with a list containing three formulae:

  1. The first formula specifies the response on the left hand side and the structure of the additive predictor for \mu parameter on the right hand side. Link function is "identity".

  2. The second formula is one sided, specifying the additive predictor for the \sigma_V on the right hand side. Link function is "log".

  3. The third formula is one sided, specifying the additive predictor for the \lambda on the right hand side. Link function is "log".

The fitted values and linear predictors for this family will be three column matrices. The first column is the \mu, the second column is the \sigma_V, the third column is \lambda. For more details of the distribution see dnormexp().

Value

An object inheriting from class general.family of the mgcv package, which can be used in the dsfa package.

References

Examples

#Set seed, sample size and type of function
set.seed(1337)
N=500 #Sample size
s=-1 #Set to production function

#Generate covariates
x1<-runif(N,-1,1); x2<-runif(N,-1,1); x3<-runif(N,-1,1)
x4<-runif(N,-1,1); x5<-runif(N,-1,1)

#Set parameters of the distribution
mu=2+0.75*x1+0.4*x2+0.6*x2^2+6*log(x3+2)^(1/4) #production function parameter
sigma_v=exp(-1.5+0.75*x4) #noise parameter
lambda=exp(-1+sin(2*pi*x5)) #inefficiency parameter

#Simulate responses and create dataset
y<-rnormexp(n=N, mu=mu, sigma_v=sigma_v, lambda=lambda, s=s)
dat<-data.frame(y, x1, x2, x3, x4, x5)

#Write formulae for parameters
mu_formula<-y~x1+x2+I(x2^2)+s(x3, bs="ps")
sigma_v_formula<-~1+x4
lambda_formula<-~1+s(x5, bs="ps")

#Fit model
model<-mgcv::gam(formula=list(mu_formula, sigma_v_formula, lambda_formula),
                 data=dat, family=normexp(s=s), optimizer = c("efs"))

#Model summary
summary(model)

#Smooth effects
#Effect of x3 on the predictor of the production function
plot(model,select=1) #Estimated function
lines(x3[order(x3)], 6*log(x3[order(x3)]+2)^(1/4)-
      mean(6*log(x3[order(x3)]+2)^(1/4)),col=2) #True effect

#Effect of x5 on the predictor of the inefficiency
plot(model,select=2) #Estimated function
lines(x5[order(x5)], -1+sin(2*pi*x5)[order(x5)]-
      mean(-1+sin(2*pi*x5)),col=2) #True effect


[Package dsfa version 1.0.1 Index]