cobbDouglasCalc function

Calculate dependent variable of a Cobb-Douglas function

Calculate the dependent variable of a Cobb-Douglas function.

cobbDouglasCalc( xNames, data, coef, coefCov = NULL, dataLogged = FALSE  )

Arguments

• xNames: 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?

Returns

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.

See Also

translogCalc, cobbDouglasOpt.

Author(s)

Arne Henningsen

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