View source: R/quadFuncDeriv.R
quadFuncDeriv | R Documentation |
Calculate the derivatives of a quadratic function.
quadFuncDeriv( xNames, data, coef, coefCov = NULL, homWeights = NULL )
xNames |
a vector of strings containing the names of the independent variables. |
data |
dataframe or a vector with named elements containing the data. |
coef |
vector containing all coefficients:
if there are |
coefCov |
optional covariance matrix of the coefficients:
the row names and column names must be the same as the names
of |
homWeights |
numeric vector with named elements that are weighting factors
for calculating an index that is used to normalize the variables
for imposing homogeneity of degree zero in these variables
(see documentation of |
Shifter variables do not need to be specified, because they have no effect on the partial derivatives. Hence, you can use this function to calculate partial derivatives even for quadratic functions that have been estimated with shifter variables.
A data frame containing the derivatives,
where each column corresponds to one of the independent variables.
If argument coefCov
is provided, it has the attributes
variance
and stdDev
,
which are two data frames containing the variances
and the standard deviations, respectively, of the derivatives.
Arne Henningsen
quadFuncEst
and quadFuncCalc
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$time <- c(1:20) # estimate a quadratic production function estResult <- quadFuncEst( "qOutput", c( "qLabor", "land", "qVarInput", "time" ), germanFarms ) # compute the marginal products of the inputs margProducts <- quadFuncDeriv( c( "qLabor", "land", "qVarInput", "time" ), germanFarms, coef( estResult ), vcov( estResult ) ) # all marginal products margProducts # their t-values margProducts / attributes( margProducts )$stdDev
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