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).
dataLogged
logical. Are the values in data already logged?
Value
A vector containing the endogenous variable.
If the inputs are provided as logarithmic values
(argument dataLogged is TRUE),
the endogenous variable is returned as logarithm;
non-logarithmic values are returned otherwise.
If argument coefCov is specified,
the returned vector has an attribute "variance"
that is a vector containing the variances
of the calculated (fitted) endogenous variable.
Author(s)
Arne Henningsen
See Also
translogCalc, cobbDouglasOpt.
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$time <- c(1:20)
# estimate a Cobb-Douglas production function
estResult <- translogEst( "qOutput", c( "qLabor", "land", "qVarInput", "time" ),
germanFarms, linear = TRUE )
# fitted values
fitted <- cobbDouglasCalc( c( "qLabor", "land", "qVarInput", "time" ), germanFarms,
coef( estResult )[ 1:5 ] )
#equal to estResult$fitted
# fitted values and their variances
fitted2 <- cobbDouglasCalc( c( "qLabor", "land", "qVarInput", "time" ), germanFarms,
coef( estResult )[ 1:5 ], coefCov = vcov( estResult )[ 1:5, 1:5 ] )
# t-values
c( fitted2 ) / attributes( fitted2 )$variance^0.5