frontier (version 1.1-8)

frontierQuad: Quadratic or Translog Frontiers

Description

This is a convenient interface for estimating quadratic or translog stochastic frontier functions using frontier.

Usage

frontierQuad( yName, xNames, shifterNames = NULL, zNames = NULL,
   data, lrTests = FALSE, … )

Arguments

yName

string: name of the endogenous variable.

xNames

a vector of strings containing the names of the X variables (exogenous variables of the production or cost function) that should be included as linear, quadratic, and interaction terms.

shifterNames

a vector of strings containing the names of the X variables that should be included as shifters only (not in quadratic or interaction terms).

zNames

a vector of strings containing the names of the Z variables (variables explaining the efficiency level).

data

a (panel) data frame that contains the data; if data is a usual data.frame, it is assumed that these are cross-section data; if data is a panel data frame (created with pdata.frame), it is assumed that these are panel data.

lrTests

logical. If TRUE, likelihood ratio tests are conducted to test the statistical significance of each X variable.

…

further arguments passed to frontier.

Value

frontierQuad returns a list of class frontierQuad (and frontier) containing the same elements as returned by frontier. If argument lrTest is set to TRUE, the returned object has a component lrTests that contains the results of likelihood-ratio tests of the statistical significance of each X variable.

See Also

frontier.

Examples

# NOT RUN {
   # example included in FRONTIER 4.1 (cross-section data)
   data( front41Data )
   front41Data$logOutput  <- log( front41Data$output )
   front41Data$logCapital <- log( front41Data$capital )
   front41Data$logLabour  <- log( front41Data$labour )

   # estimate the translog function
   translog <- frontierQuad( yName = "logOutput",
      xNames = c( "logCapital", "logLabour" ),
      data = front41Data )
   translog

   # estimate the same model using sfa()
   translog2 <- sfa( logOutput ~ logCapital + logLabour
      + I( 0.5 * logCapital^2 ) + I( logCapital * logLabour )
      + I( 0.5 * logLabour^2 ), data = front41Data )
   translog2
   all.equal( coef( translog ), coef( translog2 ),
      check.attributes = FALSE )
# }