roc.malmquist {DJL} | R Documentation |
Malmquist Index: time-series productivity analysis
Description
Employs distance measure over time to calculate the productivity changes.
Usage
roc.malmquist(xdata, ydata, tm=NULL, dm="dea", rts="crs", orientation,
g=NULL, wd=NULL, ncv=NULL, env=NULL, cv="convex")
Arguments
xdata |
Input(s) array (n by m by t) |
ydata |
Output(s) array (n by s by t) |
tm |
Tick mark of production dates (a vector length of t) |
dm |
Distance measure to calculate the productivity |
rts |
Returns to scale assumption |
orientation |
Orientation of the measurement |
g |
Directional vector indicating a measurement direction (n by (m+s)) |
wd |
Weak disposability vector indicating (an) undesirable output(s) (1 by s) |
ncv |
Non-controllable variable index(binary) for internal NDF (1 by (m+s)) |
env |
Environment index for external NDF (n by 1) |
cv |
Convexity assumption |
Value
$cu |
Catching Up (aka technical efficiency change: TEC) index |
$fs |
Frontier Shift (FS) Index |
$mi |
Malmquist Index |
Author(s)
Dong-Joon Lim, PhD
References
R. Fare, S. Grosskopf, and C. A. K. Lovell, Production Frontiers. Cambridge University Press, 1994.
See Also
dm.ddf
Distance measure using DDF
dm.dea
Distance measure using DEA
dm.hdf
Distance measure using HDF
dm.sbm
Distance measure using SBM
dm.sf
Distance measure using SF
Examples
# Load data
df <- array(c(4, 3, 9, 10, 7, 4, 3, 5,
5, 12, 3, 8, 1, 4, 14, 3,
1, 1, 1, 1, 1, 1, 1, 1,
3.4, 2, 10, 8, 10, 4, 1, 5,
6, 10, 3.5, 7, 2, 4, 12, 3,
1, 1, 1, 1, 1, 1, 1, 1,
2.8, 1.8, 8, 7, 10, 3, 1, 5,
5.7, 8.8, 2.8, 5, 2, 5, 9, 3,
1, 1, 1, 1, 1, 1, 1, 1,
2.2, 1.5, 8, 5, 8, 3, 1, 5,
6, 8, 2.3, 3.5, 2, 5, 7, 3,
1, 1, 1, 1, 1, 1, 1, 1),
c(8, 3, 4))
# Run
roc.malmquist(df[,1:2,], df[,3,], dm = "sbm", orientation = "n")