translogEla: Elasticities of a translog Function

In micEcon: Microeconomic Analysis and Modelling

Description

Calculate the elasticities of a translog function.

Usage

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

## S3 method for class 'translogEst'
elas( object, data = NULL, dataLogged = NULL,
   ... )

Arguments

xNames	a vector of strings containing the names of the independent variables.
data	dataframe containing the data; if argument data of elas.translogEst is not specified, the data frame that was used for the estimation is used for calculating elasticities.
coef	vector containing all coefficients: if there are n exogenous variables in xNames, the n+1 alpha coefficients must have names a_0, ..., a_n and the n*(n+1)/2 beta coefficients must have names b_1_1, ..., b_1_n, ..., b_n_n (only the elements of the upper right triangle of the beta matrix are directly obtained from coef; the elements of the lower left triangle are obtained by assuming symmetry of the beta matrix).
coefCov	optional covariance matrix of the coefficients: the row names and column names must be the same as the names of coef.
dataLogged	logical. Are the values in data already logged? If argument dataLogged of elas.translogEst is not specified, the same value as used in translogEst for creating object is used.
object	object of class translogEst (returned by translogEst).
...	currently ignored.

Details

Shifter variables do not need to be specified, because they have no effect on the elasticities. Hence, you can use this function to calculate elasticities even for translog functions that have been estimated with shifter variables.

Value

A data frame containing the elasticities, 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 elasticities.

Author(s)

Arne Henningsen

See Also

translogEst and translogCalc

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 quadratic production function
   estResult <- translogEst( "qOutput", c( "qLabor", "land", "qVarInput", "time" ),
      germanFarms )

   # calculate production elasticities of all inputs
   estEla <- translogEla( c( "qLabor", "land", "qVarInput", "time" ), 
      data = germanFarms, coef = coef( estResult ), 
      coefCov = vcov( estResult ) )
   # all elasticities
   estEla
   # t-values of all elasticities
   estEla / attributes( estEla )$stdDev

Example output

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        qLabor     land  qVarInput       time
1   9.36187138 4.061537 -4.1528584  0.2206473
2   6.51016645 3.064446 -2.7296682  0.2272734
3   5.44753531 2.491485 -1.6153611  0.1349494
4   3.45748658 2.299032 -1.3633733  0.1599944
5   2.74371394 2.073200 -0.7020118  0.0779936
6   2.26636928 1.717426 -0.2348715  0.0826759
7   2.11540980 1.262714  0.1243636  0.1319830
8   1.54485031 1.632592  0.4067962 -0.1088120
9   0.90006667 1.433482  0.9489289 -0.1121293
10  0.34822347 1.751032  0.7857228 -0.2602896
11 -0.04997076 1.470426  0.7608337 -0.1721908
12 -1.40266304 2.447104 -0.6568077 -0.3719662
13 -1.64792944 2.270413 -0.8267302 -0.3158886
14 -1.08571410 1.952775 -0.5682264 -0.3222459
15 -0.74955311 1.969143  0.1109939 -0.4931850
16 -0.18842508 1.631745  0.5605288 -0.5044392
17  0.43760902 1.751831  0.6002023 -0.6716586
18  0.85583669 1.691641  0.6000249 -0.7328956
19  1.20172396 1.502551  0.3745241 -0.7036584
20  1.35131846 1.277384  0.1269154 -0.6345313
          qLabor       land    qVarInput        time
1   0.1406956129 0.23616992 -0.075369419  0.04164229
2   0.0976864969 0.18051877 -0.049337105  0.04303581
3   0.0818684532 0.14838795 -0.029219820  0.02564460
4   0.0518927402 0.13670694 -0.024584726  0.03042533
5   0.0412684524 0.12399420 -0.012678976  0.01488195
6   0.0340621232 0.10305546 -0.004237629  0.01578577
7   0.0317087851 0.07594272  0.002238248  0.02516315
8   0.0232973112 0.09853102  0.007362565 -0.02081924
9   0.0135955941 0.08681402  0.017190104 -0.02155260
10  0.0052699448 0.10587411  0.014256905 -0.04994963
11 -0.0007534518 0.08870297  0.013752079 -0.03294497
12 -0.0211265593 0.14524569 -0.011851762 -0.07031732
13 -0.0247308713 0.13434649 -0.014864736 -0.05944508
14 -0.0162700912 0.11600503 -0.010210870 -0.06051480
15 -0.0112962771 0.11804312  0.002006515 -0.09314983
16 -0.0028392034 0.09837805  0.010139116 -0.09532528
17  0.0066049692 0.10590658  0.010887053 -0.12651216
18  0.0129076089 0.10236701  0.010884911 -0.13752803
19  0.0180503369 0.09071677  0.006772667 -0.13111370
20  0.0202010867 0.07687673  0.002285675 -0.11744323

micEcon documentation built on Jan. 7, 2021, 3:01 p.m.