teradial: Radial Measure of Technical Efficiency, the Debrue-Farrell...
In npsf: Nonparametric and Stochastic Efficiency and Productivity Analysis

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Routine teradial computes radial Debrue-Farrell input- or output-based measure of efficiency via reduced linear programing. In input-based radial efficiency measurement, this measure allows for proportional reductions in each positive input, and this is what permits it to shrink an input vector all the way back to the efficient subset. In output-based radial efficiency measurement, the Debrue-Farrell measure allows for proportional expansions of each positive output.

teradial(formula, data, subset,
 rts = c("C", "NI", "V"),
 base = c("output", "input"),
 ref = NULL, data.ref = NULL, subset.ref = NULL,
 intensity = FALSE,
 print.level = 1)

`formula`	an object of class “formula” (or one that can be coerced to that class): a symbolic description of the model. The details of model specification are given under ‘Details’.
`data`	an optional data frame containing the variables in the model. If not found in data, the variables are taken from environment (`formula`), typically the environment from which `teradial` is called.
`subset`	an optional vector specifying a subset of observations for which technical efficiency is to be computed.
`rts`	character or numeric. string: first letter of the word “c” for constant, “n” for non-increasing, or “v” for variable returns to scale assumption. numeric: 3 for constant, 2 for non-increasing, or 1 for variable returns to scale assumption.
`base`	character or numeric. string: first letter of the word “o” for computing output-based or “i” for computing input-based technical efficiency measure. string: 2 for computing output-based or 1 for computing input-based technical efficiency measure
`ref`	an object of class “formula” (or one that can be coerced to that class): a symbolic description of inputs and outputs that are used to define the technology reference set. The details of technology reference set specification are given under ‘Details’. If reference is not provided, the technical efficiency measures for data points are computed relative to technology based on data points themselves.
`data.ref`	an optional data frame containing the variables in the technology reference set. If not found in `data.ref`, the variables are taken from environment(ref), typically the environment from which `teradial` is called.
`subset.ref`	an optional vector specifying a subset of observations to define the technology reference set.
`intensity`	logical. If set to TRUE, the value `intensity` will contain `K x Kref` matrix with intensity variables, which can be used to for example identify the peers. Default is `FALSE` as the matris is potentially large.
`print.level`	numeric. 0 - nothing is printed; 1 - print summary of the model and data. 2 - print summary of technical efficiency measures. 3 - print estimation results observation by observation. Default is 1.

Routine teradial computes the radial output- or input-based measure of technical efficiency under assumption of constant, non-increasing, or variable returns to scale technology. The details of the estimator can be found e.g., in Färe, Grosskopf, and Lovell (1994, especially section 3.1 on p.62 fot input-based and section 4.1 on p.96 for output-based efficiency measurement) or Badunenko and Mozharovskyi (2016).

Models for teradial are specified symbolically. A typical model has the form outputs ~ inputs, where outputs (inputs) is a series of (numeric) terms which specifies outputs (inputs). The same goes for reference set. Refer to the examples.

Results can be summarized using summary.npsf.

teradial returns a list of class npsf containing the following elements:

`K`	numeric: number of data points.
`M`	numeric: number of outputs.
`N`	numeric: number of inputs.
`rts`	string: RTS assumption.
`base`	string: base for efficiency measurement.
`te`	numeric: radial measure (Debrue-Farrell) of technical efficiency.
`intensity`	numeric: if the option `intensity` is set to TRUE, the value `intensity` will contain `K x Kref` matrix with intensity variables, which can be used to for example identify the peers (see example with `t3` in the example section). Is `NULL` if option `intensity` is set to `FALSE`, which is a default.
`esample`	logical: returns TRUE if the observation in user supplied data is in the estimation subsample and FALSE otherwise.
`esample.ref`	logical: returns TRUE if the observation in the user supplied reference is in the reference subsample and FALSE otherwise.

In case of one input (output), the input (output)-based Debrue-Farrell measure is equal to Russell measure of technical efficiency (see tenonradial).

Results can be summarized using summary.npsf.

Oleg Badunenko <oleg.badunenko@brunel.ac.uk>, Pavlo Mozharovskyi <pavlo.mozharovskyi@telecom-paris.fr>

Badunenko, O. and Mozharovskyi, P. (2016), Nonparametric Frontier Analysis using Stata, Stata Journal, 163, 550–89, doi: 10.1177/1536867X1601600302

Färe, R. and Lovell, C. A. K. (1978), Measuring the technical efficiency of production, Journal of Economic Theory, 19, 150–162, doi: 10.1016/0022-0531(78)90060-1

Färe, R., Grosskopf, S. and Lovell, C. A. K. (1994), Production Frontiers, Cambridge U.K.: Cambridge University Press, doi: 10.1017/CBO9780511551710

tenonradial, teradialbc, tenonradialbc, nptestrts, nptestind, sf

require( npsf )

# Prepare data and matrices

data( pwt56 )
head( pwt56 )

# Create some missing values

pwt56 [49, "K"] <- NA # create missing

Y1 <- as.matrix ( pwt56[ pwt56$year == 1965, c("Y"), drop = FALSE] )
X1 <- as.matrix ( pwt56[ pwt56$year == 1965, c("K", "L"), drop = FALSE] )

X1 [51, 2] <- NA # create missing
X1 [49, 1] <- NA # create missing

data( ccr81 )
head( ccr81 )

# Create some missing values

ccr81 [64, "x4"] <- NA # create missing
ccr81 [68, "y2"] <- NA # create missing

Y2 <- as.matrix( ccr81[ , c("y1", "y2", "y3"), drop = FALSE] )
X2 <- as.matrix( ccr81[ , c("x1", "x2", "x3", "x4", "x5"), drop = FALSE] )

# Computing without reference set

# Using formula

# Country is a categorical variable, so nonradial gives error message

# t1 <- teradial ( Country ~ K + L, data = pwt56 )

# for computing the efficiencies of countries in 1965 
# with technology reference set is defined by observations in 1965
# (that same sample of countries)

t2 <- teradial ( Y ~ K + L, data = pwt56, rts = "v", 
base = "in", print.level = 2)

# Using a subset

t3 <- teradial ( Y ~ K + L, data = pwt56, subset = year == 1965,
	rts = "VRS", base = "in", print.level = 3, intensity = TRUE )

# VRS constraint is satisfied, which is easy to varify 
# by checking the sums of intensity variables
rowSums(t3$intensity)

# to obtain peers create a list that will contain order numers of peers
t3.peers <- list()
# now fill this list
for(i in seq.int(sum(t3$esample))){
  t3.peers[[i]] <- which( t3$intensity[i,] != 0 )
}

t4 <- teradial ( Y ~ K + L, data = pwt56, subset = Nu < 10,
	rts = "vrs", base = "I" )

t5 <- teradial ( Y ~ L, data = pwt56, subset = Nu < 10, rts = "v" )

# Multiple outputs

t8 <- teradial ( y1 + y2 + y3 ~ x1 + x2 + x3 + x4 + x5, data = ccr81,
	rts = "v", base = "i" )

# Using a subset

t7 <- teradial ( y1 + y2 + y3 ~ x1 + x2 + x3 + x4 + x5, data = ccr81,
	subset = x5 != 22, rts = "n", base = "o" )

# Computation using matrices

t9 <- teradial ( Y1 ~ X1, rts = "v", base = "i" )

# Define subsets on a fly

t10 <- teradial ( Y1[-1,] ~ X1[-2,1] )
t11 <- teradial ( Y1[-3,] ~ X1[-1,], rts = "v", base = "o" )

# Multiple outputs

t12 <- teradial ( Y2 ~ X2 )
t13 <- teradial ( Y2[-66,] ~ X2[-1, -c(1,3)] )


# Computing with reference set

# Using formula

# For computing the efficiencies of countries with order number
# less than 10 with technology reference set defined by countries
# with order number larger than 10 and smaller than 11 (in effect 
# no reference set, hence warning) type

t14 <- teradial ( Y ~ K + L, data = pwt56, subset = Nu < 10, 
	ref = Y ~ K + L, data.ref = pwt56,
	subset.ref = Nu > 10 & Nu < 11 ) # warning

# For computing the efficiencies of countries with order number
# less than 10 with technology reference set defined by countries 
# with order number larger than 10 and smaller than 15 type

t15 <- teradial ( Y ~ K + L, data = pwt56, subset = Nu < 10, ref = Y ~ K + L, 
	data.ref = pwt56, subset.ref = Nu > 10 & Nu < 15 )

# For computing the efficiencies of countries in 1965 
# with technology reference set is defined by observations in both
# 1965 and 1990 (all) type
	
t16 <- teradial ( Y ~ K + L, data = pwt56, subset = year == 1965,
	rts = "v", base = "i", 
	ref = Y ~ K + L, data.ref = pwt56 )

# For computing the efficiencies of countries in 1990
# with technology reference set is defined by observations in 1965
# type

t17 <- teradial ( Y ~ K + L, data = pwt56, subset = year == 1990, 
	ref = Y ~ K + L, data.ref = pwt56, subset.ref = year == 1965 )

# Using matrices

t18 <- teradial ( Y1[-1,] ~ X1[-2,], ref = Y1[-2,] ~ X1[-1,] )

# error: not equal number of observations in outputs and inputs

# t19 <- teradial ( Y1[-1,] ~ X1[-(1:2),], 
# ref = Y1[-2,] ~ X1[-1,1] )

# Combined formula and matrix

# error: not equal number of inputs in data and reference set

# t20 <- teradial ( Y ~ K + L, data = pwt56, subset = Nu < 10,
# ref = Y1[-2,] ~ X1[-1,1] )

t21 <- teradial ( Y ~ K + L, data = pwt56, subset = Nu < 10, 
	ref = Y1[-2,] ~ X1[-1,] )
	
## Not run: 

# Really large data-set

data(usmanuf)
head(usmanuf)

nrow(usmanuf)
table(usmanuf$year)

# This will take some time depending on computer power

t22 <- teradial ( Y ~ K + L + M, data = usmanuf, 
	subset = year >= 1995 & year <= 2000 ) 

# Summary

summary ( t22$te )

# Write efficiencies to the data frame:

usmanuf$te_nonrad_crs_out[ t22$esample ] <- t22$te

head(usmanuf, 17)


## End(Not run)