roc.malmquist: Malmquist Index: time-series productivity analysis
In DJL: Distance Measure Based Judgment and Learning

roc.malmquist

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 `"dea"` Data Envelopment Analysis (default) `"sbm"` Slack Based Model `"ddf"` Directional Distance Function `"hdf"` Hyperbolic Distance Function `"sf"` Shortage Function
`rts`	Returns to scale assumption `"crs"` Constant RTS (default) `"vrs"` Variable RTS `"irs"` Increasing RTS `"drs"` Decreasing RTS
`orientation`	Orientation of the measurement `"n"` Non-orientation (default) `"i"` Input-orientation `"o"` Output-orientation
`g`	Directional vector indicating a measurement direction (n by (m+s)) By default (`NULL`), `xdata` & `ydata` will be used
`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 `"convex"` Convexity holds (default) `"fdh"` Free disposal hull (this will override `rts`)

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.

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")

DJL documentation built on March 31, 2023, 9:05 p.m.