spfrontier (version 0.2.5)

spfrontier: Spatial stochastic frontier model

Description

spfrontier estimates spatial specifications of the stochastic frontier model.

Usage

spfrontier(
  formula,
  data,
  W_y = NULL,
  W_v = NULL,
  W_u = NULL,
  inefficiency = "half-normal",
  initialValues = "errorsarlm",
  logging = c("quiet", "info", "debug"),
  control = NULL,
  onlyCoef = F,
  costFrontier = F
)

Arguments

formula

an object of class "formula": a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.

data

data frame, containing the variables in the model

W_y

a spatial weight matrix for spatial lag of the dependent variable

W_v

a spatial weight matrix for spatial lag of the symmetric error term

W_u

a spatial weight matrix for spatial lag of the inefficiency error term

inefficiency

sets the distribution for inefficiency error component. Possible values are 'half-normal' (for half-normal distribution) and 'truncated' (for truncated normal distribution). By default set to 'half-normal'. See references for explanations

initialValues

an optional vector of initial values, used by maximum likelihood estimator. If not defined, estimator-specific method of initial values estimation is used.

logging

an optional level of logging. Possible values are 'quiet','warn','info','debug'. By default set to quiet.

control

an optional list of control parameters, passed to optim estimator from the 'stats package

onlyCoef

allows calculating only estimates for coefficients (with inefficiencies and other additional statistics). Developed generally for testing, to speed up the process.

costFrontier

is designed for selection of cost or production frontier

Details

Models for estimation are specified symbolically, but without any spatial components. Spatial components are included implicitly on the base of the model argument.

References

Kumbhakar, S.C. and Lovell, C.A.K (2000), Stochastic Frontier Analysis, Cambridge University Press, U.K.

Examples

# NOT RUN {
data( airports )
airports2011 <- subset(airports, Year==2011)
W <- constructW(cbind(airports2011$longitude, airports2011$latitude),airports2011$ICAO)
formula <- log(PAX) ~ log(Population100km) + log(Routes) + log(GDPpc)
ols <- lm(formula , data=airports2011)
summary(ols )
plot(density(stats::residuals(ols)))
skewness(stats::residuals(ols))

# Takes >5 sec, see demo for more examples
# model <- spfrontier(formula , data=airports2011)
# summary(model )

# model <- spfrontier(formula , data=airports2011, W_y=W)
# summary(model )

# }