a vector of strings containing the names of the
independent variables.
data
data frame containing the data.
coef
vector containing the coefficients:
if the elements of the vector have no names,
the first element is taken as intercept of the logged equation
and the following elements are taken as coefficients of
the independent variables defined in argument xNames
(in the same order);
if the elements of coef have names,
the element named a_0 is taken as intercept of the logged
equation
and the elements named a_1, ..., a_n
are taken as coefficients of the independent variables
defined in argument xNames (numbered in that order).
coefCov
optional covariance matrix of the coefficients
(the order of the rows and columns must correspond
to the order of the coefficients in argument coef).
yName
an optional string containing the name of the dependent
variable.
If it is NULL, the dependent variable is calculated
from the independent variables and the coefficients.
dataLogged
logical. Are the values in data already logged?
Value
a list of class cobbDouglasDeriv containing following objects:
deriv
data frame containing the derivatives.
variance
data frame containing the variances of the derivatives
(only if argument coefCov is provided).
NOTE: if argument yName is specified,
the variance of the endogenous variable is currently ignored.
Author(s)
Arne Henningsen
See Also
cobbDouglasCalc, translogDeriv.
Examples
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$time <- c(1:20)
# estimate a Cobb-Douglas production function
estResult <- translogEst( "qOutput", c( "qLabor", "qVarInput", "land", "time" ),
germanFarms, linear = TRUE )
# compute the marginal products of the inputs (with "fitted" Output)
margProducts <- cobbDouglasDeriv( c( "qLabor", "qVarInput", "land", "time" ),
data = germanFarms, coef = coef( estResult )[1:5],
coefCov = vcov( estResult )[1:5,1:5] )
margProducts$deriv
# t-values
margProducts$deriv / margProducts$variance^0.5
# compute the marginal products of the inputs (with observed Output)
margProductsObs <- cobbDouglasDeriv( c( "qLabor", "qVarInput", "land", "time" ),
data = germanFarms, coef = coef( estResult )[1:5], yName = "qOutput",
coefCov = vcov( estResult )[1:5,1:5] )
margProductsObs$deriv
# t-values
margProductsObs$deriv / margProductsObs$variance^0.5