systemfit (version 0.8-0)

predict.systemfit: Predictions from System Estimation

Description

Returns the predicted values, their standard errors and the confidence limits of prediction.

Usage

## S3 method for class 'systemfit':
predict( object, data = object$data,
                             se.fit = FALSE, se.pred = FALSE,
                             interval = "none", level=0.95, ... )
## S3 method for class 'systemfit.equation':
predict( object, data, ... )

Arguments

object

an object of class systemfit or systemfit.equation.

data

data frame in which to predict.

se.fit

return the standard error of the fitted values?

se.pred

return the standard error of prediction?

interval

Type of interval calculation ("none", "confidence" or "prediction")

level

Tolerance/confidence level.

...

additional optional arguments.

Value

• predict.systemfit returns a dataframe that contains for each equation the predicted values (e.g. "eq1.pred") and if requested the standard errors of the fitted values (e.g. "eq1.se.fit"), the standard errors of the prediction (e.g. "eq1.se.pred"), and the lower (e.g. "eq1.lwr") and upper (e.g. "eq1.upr") limits of the confidence or prediction interval(s).

  predict.systemfit.equation returns a vector of the predicted values of a single equation.

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 $Var[y^0-\hat{y}^0]=\hat{\sigma}^2+x^0 \; Var[b] \; {x^0}'$

References

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.

See Also

systemfit, predict

Examples

data( "Kmenta" )
demand <- consump ~ price + income
supply <- consump ~ price + farmPrice + trend
labels <- list( "demand", "supply" )
system <- list( demand, supply )

## OLS estimation
fitols <- systemfit("OLS", system, labels, data=Kmenta )

## predicted values and limits
predict( fitols )

## predicted values of the first equation
predict( fitols$eq[[1]] )

## predicted values of the second equation
predict( fitols$eq[[2]] )