Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/translogCheckMono.R
Check monotonicity of a translog function.
1 2 3 4 5 6 7 8 9 10 11 12 | translogCheckMono( xNames, data, coef, increasing = TRUE,
strict = FALSE, dataLogged = FALSE,
tol = 10 * .Machine$double.eps )
## S3 method for class 'translogCheckMono'
print( x, ... )
## S3 method for class 'translogCheckMono'
summary( object, ... )
## S3 method for class 'summary.translogCheckMono'
print( x, ... )
|
xNames |
a vector of strings containing the names of the independent variables. |
data |
dataframe containing the data. |
coef |
vector containing all coefficients. |
increasing |
single logical value or vector of logical values
of the same length as argument |
strict |
logical. Check for strict (TRUE) or non-strict (default, FALSE) monotonicity? |
dataLogged |
logical. Are the values in |
tol |
tolerance level for checking non-strict monotonicity:
values between |
x |
an object returned by |
object |
an object returned by |
... |
currently not used. |
Function translogCheckMono internally calls
function translogDeriv
and then checks if the derivatives have the sign
specified in argument increasing.
Function translogCheckMono does not have an argument
shifterNames, because shifter variables do not affect
the monotonicity conditions of the eplanatory variables
defined in Argument xNames.
Therefore, translogCheckMono automatically removes
all coefficients of the shifter variables
before it calls translogDeriv.
translogCheckMono returns a list of class translogCheckMono
containing following objects:
obs |
a vector indicating whether monotonicity is fulfilled at each observation. |
exog |
data frame indicating whether monotonicity is fulfilled for each exogenous variable at each observation. |
increasing |
argument |
strict |
argument |
Arne Henningsen
translogEst, translogDeriv,
and translogCheckCurvature
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | data( germanFarms )
# output quantity:
germanFarms$qOutput <- germanFarms$vOutput / germanFarms$pOutput
# quantity of variable inputs
germanFarms$qVarInput <- germanFarms$vVarInput / germanFarms$pVarInput
# a time trend to account for technical progress:
germanFarms$time <- c(1:20)
# estimate a translog production function
estResult <- translogEst( "qOutput", c( "qLabor", "land", "qVarInput", "time" ),
germanFarms )
# check whether the production function is monotonically increasing
# in all inputs
test <- translogCheckMono( xNames = c( "qLabor", "land", "qVarInput", "time" ),
data = germanFarms, coef = coef( estResult ) )
test
summary( test )
# check whether the production function is monotonically decreasing
# in time and monotonically increasing in all other inputs
test <- translogCheckMono( c( "qLabor", "land", "qVarInput", "time" ),
germanFarms, coef( estResult ), increasing = c( TRUE, TRUE, TRUE, FALSE ) )
test
summary( test )
|
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This translog function is monotonically increasing in qLabor, land, qVarInput, time at 1 out of 20 observations (5%)
This translog function is monotonically increasing in qLabor, land, qVarInput, time at 1 out of 20 observations (5%)
The monotonicity condition for the exogenous variable
- 'qLabor' is fulfilled at 14 out of 20 observations (70%)
- 'land' is fulfilled at 20 out of 20 observations (100%)
- 'qVarInput' is fulfilled at 11 out of 20 observations (55%)
- 'time' is fulfilled at 7 out of 20 observations (35%)
This translog function is monotonically increasing in qLabor, land, qVarInput and monotonically decreasing in time at 7 out of 20 observations (35%)
This translog function is monotonically increasing in qLabor, land, qVarInput and monotonically decreasing in time at 7 out of 20 observations (35%)
The monotonicity condition for the exogenous variable
- 'qLabor' is fulfilled at 14 out of 20 observations (70%)
- 'land' is fulfilled at 20 out of 20 observations (100%)
- 'qVarInput' is fulfilled at 11 out of 20 observations (55%)
- 'time' is fulfilled at 13 out of 20 observations (65%)
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