Benchmarking (version 0.10)

Benchmarking-package: Benchmarking: Benchmark and efficiency: deterministic (DEA) and stochastic frontiers (SFA) -- Estimation and plot of technologies

Description

Calculates efficiency for DEA models under various technology assumptions, allowing for slacks if wanted Calculates super efficiency in DEA models Bootstrap of DEA models, test in DEA models Malmqvist index for DEA models Estimate stochastic frontier production function (SFA) Plot of DEA technologies for two goods; either a transformation curve (2 outputs), isoquant (2 inputs), or a production function (1 input og 1 output).

Details

ll{ Package: Benchmarking Type: Package Version: 0.10 ($Revision: 72 $) Date: $Date: 2010-09-11 19:06:14 +0200 (11 sep 2010) $ License: Copyright } ll{ dea Estimate DEA input or output efficience measures, peers, lambdas and slacks slack calculate slacks in DEA models} dea.dual calculates dual weights (prices), including restrictions on weights dea.merge calculate and decompose efficiency from a merger of firms sdea calculates super efficiency. sfa estimate stochastic frontier production function cost.opt Calculate the optimal input for given output and prices; accept negative input and output. revenue.opt Calculate the optimal output for given input and prices; accept negative input and output. profit.opt Calculate the optimal input and output for given input and output prices. dea.plot making DEA graphs under alternative technology assumptions. dea.plot.frontier is specialized for 1 input and 1 output. dea.plot.isoquant is specialized for 2 inputs. dea.plot.transform is specialized for 2 outputs. critValue Critical values calculated from bootstraped DEA models. typeIerror Probability of a type I error for a test in bootstraped DEA models.

References

Andersen & Petersen (1993) FEAR (Wilson 2008)

See Also

FEAR (Wilson 2008)

Examples

# Plot of different technologies
x <- matrix(c(100,200,300,500),ncol=1,dimnames=list(LETTERS[1:4],"x"))
y <- matrix(c(75,100,300,400),ncol=1,dimnames=list(LETTERS[1:4],"y"))
dea.plot(x,y,RTS="vrs",ORIENTATION="in-out",txt=rownames(x))
dea.plot(x,y,RTS="drs",ORIENTATION="in-out",add=TRUE,lty="dashed",lwd=2)
dea.plot(x,y,RTS="crs",ORIENTATION="in-out",add=TRUE,lty="dotted")
                      
                      
dea.plot(x,y,RTS="fdh",ORIENTATION="in-out",txt=rownames(x),main="fdh")
dea.plot(x,y,RTS="irs",ORIENTATION="in-out",txt=rownames(x),main="irs")
dea.plot(x,y,RTS="irs2",ORIENTATION="in-out",txt=rownames(x),main="irs2")
# dea.plot(x,y,RTS="add",ORIENTATION="in-out",txt=rownames(x))


#  A quick frontier with 1 input and 1 output
dea.plot(x,y, main="Basic plot of frontier")


# Calculating of efficiency
dea(x,y, RTS="vrs", ORIENTATION="in")
e <- dea(x,y, RTS="vrs", ORIENTATION="in")
e
eff(e)
peers(e)
matrix(rownames(x)[peers(e)],ncol=dim(x)[1])
print(
	matrix(rownames(x)[peers(e)],ncol=dim(x)[1], 
	dimnames=list(c( paste("r",seq(1,dim(peers(e))[2]),sep="") ), rownames(x)) )
, quote=FALSE)
lambda(e)


# Calculating of super efficiency
sdea(x,y, RTS="vrs", ORIENTATION="in")
# Technology for super efficiency for firm number 3/C 
dea.plot(x[-3],y[-3],RTS="vrs",ORIENTATION="in-out",txt=LETTERS[c(1,2,4)])
points(x[3],y[3],cex=2)
text(x[3],y[3],LETTERS[3],adj=c(-.75,.75))
e3 <- dea(x,y, XREF=x[-3,], YREF=y[-3,])
eff(e3)
peers(e3)
print(matrix(rownames(x)[peers(e3)],ncol=dim(x)[1]),quote=FALSE)
lambda(e3)
e3$lambda



# Taking care of slacks
x <- matrix(c(100,200,300,500,100,600),ncol=1,
        dimnames=list(LETTERS[1:6],"x"))
y <- matrix(c(75,100,300,400,50,400),ncol=1,
        dimnames=list(LETTERS[1:6],"y"))

# Phase one
e <- dea(x,y)
print(e)
peers(e)
lambda(e)
# Phase two
sl <- slack(x,y,e)
data.frame(sl$sx,sl$sy)
peers(sl)
lambda(sl)
sl$lambda

# Slacks, two phases in one
e2 <- dea(x,y,SLACK=TRUE)
print(e2)
data.frame(eff(e2),e2$slack,e2$sx,e2$sy,lambda(e2))
peers(e2)
lambda(e2)
e2$lambda