predict.snqProfitEst function

Predictions from an SNQ profit function

Predictions from an SNQ profit function

Returns the predicted values, their standard errors and the confidence limits of prediction for an Symmetric Normalized Quadratic (SNQ) profit function.

## 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[y0]y^0]=x0  Var[b]  x0Var[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[y0y^0]=σ^2+x0  Var[b]  x0Var[y^0-\hat{y}^0]=\hat{\sigma}^2+x^0 \; Var[b] \; {x^0}'

Returns

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).

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.

Author(s)

Arne Henningsen

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" ) }