predict.snqProfitEst {micEconSNQP} | R Documentation |
Predictions from an SNQ profit function
Description
Returns the predicted values, their standard errors and the confidence limits of prediction for an Symmetric Normalized Quadratic (SNQ) profit function.
Usage
## S3 method for class 'snqProfitEst'
predict( object, newdata = object$data,
se.fit = FALSE, se.pred = FALSE, interval = "none", level = 0.95,
useDfSys = TRUE, ... )
## S3 method for class 'snqProfitImposeConvexity'
predict( object, newdata = object$data,
se.fit = FALSE, se.pred = FALSE, interval = "none", level = 0.95,
useDfSys = TRUE, ... )
Arguments
object |
an object of type |
newdata |
data frame in which to predict. |
se.fit |
logical. Return the standard error of the fitted values? |
se.pred |
logical. Return the standard error of prediction? |
interval |
Type of interval calculation ("none", "confidence" or "prediction"). |
level |
confidence level. |
useDfSys |
logical. Use the degrees of freedom of the whole system (and not the degrees of freedom of the single equation) to calculate the confidence intervals. |
... |
currently not used. |
Details
The variance of the fitted values
(used to calculate the standard errors of the fitted values
and the "confidence interval") is calculated by
Var[E[y^0]-\hat{y}^0]=x^0 \; Var[b] \; {x^0}'
The variances of the predicted values
(used to calculate the standard errors of the predicted values
and the "prediction intervals") is calculated by
Var[y^0-\hat{y}^0]=\hat{\sigma}^2+x^0 \; Var[b] \; {x^0}'
Value
predict.snqProfitEst
and predict.snqProfitImposeConvexity
return
a dataframe that
contains the predicted profit and for each netput the predicted quantities
(e.g. "quant1" ) and if requested
the standard errors of the fitted values (e.g. "quant1.se.fit"),
the standard errors of the prediction (e.g. "quant1.se.pred"),
and the lower (e.g. "quant1.lwr") and upper (e.g. "quant1.upr")
limits of the confidence or prediction interval(s).
Author(s)
Arne Henningsen
References
Diewert, W.E. and T.J. Wales (1987) Flexible functional forms and global curvature conditions. Econometrica, 55, p. 43-68.
Diewert, W.E. and T.J. Wales (1992) Quadratic Spline Models for Producer's Supply and Demand Functions. International Economic Review, 33, p. 705-722.
Greene, W. H. (2003) Econometric Analysis, Fifth Edition, Macmillan.
Gujarati, D. N. (1995) Basic Econometrics, Third Edition, McGraw-Hill.
Kmenta, J. (1997) Elements of Econometrics, Second Edition, University of Michigan Publishing.
Kohli, U.R. (1993) A symmetric normalized quadratic GNP function and the US demand for imports and supply of exports. International Economic Review, 34, p. 243-255.
See Also
snqProfitEst
, snqProfitCalc
and predict
Examples
if( requireNamespace( 'micEcon', quietly = TRUE ) ) {
data( germanFarms, package = "micEcon" )
germanFarms$qOutput <- germanFarms$vOutput / germanFarms$pOutput
germanFarms$qVarInput <- -germanFarms$vVarInput / germanFarms$pVarInput
germanFarms$qLabor <- -germanFarms$qLabor
germanFarms$time <- c( 0:19 )
priceNames <- c( "pOutput", "pVarInput", "pLabor" )
quantNames <- c( "qOutput", "qVarInput", "qLabor" )
estResult <- snqProfitEst( priceNames, quantNames, c("land","time"), data=germanFarms )
predict( estResult )
predict( estResult, se.fit = TRUE, se.pred = TRUE, interval = "confidence" )
}