predict.snqProfitEst {micEconSNQP}

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 snqProfitEst or snqProfitImposeConvexity.

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" )
}