compute_malmquist {pioneeR} | R Documentation |
Compute Malmquist
Description
Calculate the Malmquist productivity index and its components using Data Envelopment Analysis.
Usage
compute_malmquist(data, input, output, id, time, orientation = c("in", "out"))
Arguments
data |
Dataset to analyse. |
input |
A character vector with input variables. |
output |
A character vector with output variables. |
id |
A string with the DMU id or name variable. |
time |
A string with the time period variable. |
orientation |
Model orientation. |
Details
Results are returned a la Farrell. This implies that for output-oriented models values above one signify improvements in productivity, while values less than one imply deterioration in productivity. For input-oriented models the interpretation is reversed; values less than one denote improvements and values above one denote deterioration.
Note that compute_malmquist()
only works for balanced panel datasets.
Value
A list of class pioneer_mlm
References
Färe, R., Grosskopf, S. (1996). Intertemporal production frontiers: With dynamic DEA. Springer.
Examples
# Load example data
chnEconomy <- deaR::EconomyLong
# Estimate Malmquist
mod <- compute_malmquist(
data = chnEconomy,
id = 'DMUs',
time = 'Period',
input = c('Labor', 'Capital'),
output = 'GIOV',
orientation = 'in')
# Print results
print(mod)
# Get summary
summary(mod)
# Convert to data frame
df <- as.data.frame(mod)