| comper_mv {dsfa} | R Documentation |
The comper implements the composed-error distribution in which the \mu, \sigma_V and \sigma_U can depend on additive predictors.
Useable with mgcv::gam, the additive predictors are specified via a list of formulae.
comper_mv(
link = list("identity", "log", "log", "identity", "log", "log", "glogit"),
s = c(-1, -1),
distr = c("normhnorm", "normhnorm", "normal"),
rot = 0
)
link |
three item list, specifying the link for the |
s |
integer vector of length two; each element corresponds to one marginal. |
distr |
string vector of length three; the first two elements determine the distribution of the marginals. Available are: |
rot |
integer determining the rotation for Archimedian copulas. Can be |
Used with gam() to fit distributional stochastic frontier model. The function is called with a list containing three formulae:
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".
The second formula is one sided, specifying the additive predictor for the \sigma_V on the right hand side. Link function is "log".
The third formula is one sided, specifying the additive predictor for the \sigma_U 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 \sigma_U.
For more details of the distribution see dcomper().
An object inheriting from class general.family of the mgcv package, which can be used in the 'mgcv' and 'dsfa' package.
Schmidt R, Kneib T (2022). “Multivariate Distributional Stochastic Frontier Models.” arXiv preprint arXiv:2208.10294.
Wood SN, Fasiolo M (2017). “A generalized Fellner-Schall method for smoothing parameter optimization with application to Tweedie location, scale and shape models.” Biometrics, 73(4), 1071–1081.
Aigner D, Lovell CK, Schmidt P (1977). “Formulation and estimation of stochastic frontier production function models.” Journal of econometrics, 6(1), 21–37.
Kumbhakar SC, Wang H, Horncastle AP (2015). A practitioner's guide to stochastic frontier analysis using Stata. Cambridge University Press.
Azzalini A (2013). The skew-normal and related families, volume 3. Cambridge University Press.
Schmidt R, Kneib T (2020). “Analytic expressions for the Cumulative Distribution Function of the Composed Error Term in Stochastic Frontier Analysis with Truncated Normal and Exponential Inefficiencies.” arXiv preprint arXiv:2006.03459.
#Set seed, sample size and type of function
set.seed(1337)
N=1000 #Sample size
s<-c(-1,-1) #Set to production function for margin 1 and set to cost function for margin 2
distr_cop="normal"
distr_marg1="normhnorm"
distr_marg2="normhnorm"
#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); x6<-runif(N,-1,1)
x7<-runif(N,-1,1)
mu1=6+2*x1+(-2/3)*x1^2 #production function parameter 1
sigma_v1=exp(-1.5+sin(2*pi*x2)) #noise parameter 1
sigma_u1=exp(-1) #inefficiency parameter 1
mu2=5*x4^2+4*log(x4+2)^(1/4) #cost function parameter 2
sigma_v2=exp(-1.5) #noise parameter 2
sigma_u2=exp(-1+sin(2*pi*x6)) #inefficiency parameter 2
delta=transform(x=matrix(1+2.5*cos(4*x7)),
type="glogitinv",
par=delta_bounds(distr_cop), deriv_order = 0)
#Simulate responses and create dataset
Y<-rcomper_mv(n=N, mu=cbind(mu1,mu2),
sigma_v=cbind(sigma_v1, sigma_v2),
sigma_u = cbind(sigma_u1, sigma_u2), s=s,
delta=delta,
distr = c(distr_marg1,distr_marg2,distr_cop))
dat<-data.frame(y1=Y[,1],y2=Y[,2], x1, x2, x3, x4, x5, x6, x7)
#Write formulae for parameters
mu_1_formula<-y1~s(x1,bs="ps")
sigma_v1_formula<-~s(x2,bs="ps")
sigma_u1_formula<-~1
mu_2_formula<-y2~s(x4,bs="ps")
sigma_v2_formula<-~1
sigma_u2_formula<-~s(x6,bs="ps")
delta_formula<-~s(x7,bs="ps")
#Fit model
model<-mgcv::gam(formula=list(mu_1_formula,sigma_v1_formula,sigma_u1_formula,
mu_2_formula,sigma_v2_formula,sigma_u2_formula,
delta_formula), data=dat,
family=comper_mv(s=s, distr=c(distr_marg1,distr_marg2,distr_cop)),
optimizer="efs")
#Model summary
summary(model)
#Smooth effects
#Effect of x1 on the predictor of the production function of margin 1
plot(model, select=1) #Estimated function
lines(x1[order(x1)], 2*x1[order(x1)]+(-1/3)*x1[order(x1)]^2-
mean(2*x1+(-1/3)*x1^2), col=2) #True effect
#Effect of x2 on the predictor of the noise of margin 1
plot(model, select=2) #Estimated function
lines(x2[order(x2)], -1.5+sin(2*pi*x2[order(x2)])-
mean(-1.5+sin(2*pi*x2)),col=2) #True effect
#Effect of x4 on the predictor of the production function of margin 2
plot(model, select=3) #Estimated function
lines(x4[order(x4)], 3+5*x4[order(x4)]^2+4*log(x4[order(x4)]+2)^(1/4)-
mean(3+5*x4^2+4*log(x4+2)^(1/4)), col=2) #True effect
#Effect of x6 on the predictor of the inefficiency of margin 2
plot(model, select=4) #Estimated function
lines(x6[order(x6)], -1+sin(2*pi*x6[order(x6)])-
mean(-1+sin(2*pi*x6)),col=2) #True effect
#Effect of x7 on the predictor of the copula
plot(model, select=5) #Estimated function
lines(x7[order(x7)], 2.5*cos(4*x7[order(x7)])-
mean(2.5*cos(4*x7)),col=2) #True effect
efficiency(model)
elasticity(model)