costDEA {hyperbolicDEA}

Cost DEA model

Description

Cost DEA model optimizing the input allocation with given prices. It returns the estimated lambdas as well as the optimal values for inputs and a cost efficiency score that is the ratio of optimal costs over observed costs.

Usage

costDEA(X, Y, pX, RTS = "crs")

Arguments

X

Vector, matrix or dataframe with DMUs as rows and inputs as columns

Y

Vector, matrix or dataframe with DMUs as rows and outputs as columns

pX

Vector, matrix or dataframe with prices for each DMU and input. Therefore it must have the same dimensions as X.

RTS

Character string indicating the returns-to-scale, e.g. "crs", "vrs".

Value

A list object containing the following:

lambdas

Estimated values for the composition of the respective Benchmarks. The lambdas are stored in a matrix with dimensions nrow(X) x nrow(X), where the row is the DMU under observation and the columns are the peers used for the Benchmark.

opt_value

Optimal inputs.

cost_eff

Cost efficiency as the ratio of the optimal cost to the observed cost.

See Also

[Benchmarking::cost.opt] for a similar function

Examples

X <- matrix(c(1,2,3,3,2,1,2,2), ncol = 2)
Y <- matrix(c(1,1,1,1), ncol = 1)

pX <- matrix(c(2,1,2,1,2,1,1,2), ncol =  2, byrow = TRUE)


cost_eff_input <- costDEA(X,Y,pX)