# se.ratio.systemfit: Ratio of the Standard Errors In systemfit: Estimating Systems of Simultaneous Equations

## Description

`se.ratio.systemfit` returns a vector of the ratios of the standard errors of the predictions for two equations.

## Usage

 `1` ```se.ratio.systemfit( resultsi, resultsj, eqni ) ```

## Arguments

 `resultsi` an object of type `systemfit`. `resultsj` an object of type `systemfit`. `eqni` index for equation to obtain the ratio of standard errors.

## Value

`se.ratio` returns a vector of the standard errors of the ratios for the predictions between the predicted values in equation i and equation j.

## Author(s)

Jeff D. Hamann [email protected]

## References

Hasenauer, H; Monserud, R and T. Gregoire. (1998) Using Simultaneous Regression Techniques with Individual-Tree Growth Models. Forest Science. 44(1):87-95

`systemfit` and `correlation.systemfit`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```data( "Kmenta" ) eqDemand <- consump ~ price + income eqSupply <- consump ~ price + farmPrice + trend inst <- ~ income + farmPrice + trend system <- list( demand = eqDemand, supply = eqSupply ) ## perform 2SLS on each of the equations in the system fit2sls <- systemfit( system, "2SLS", inst = inst, data = Kmenta ) fit3sls <- systemfit( system, "3SLS", inst = inst, data = Kmenta ) ## print the results from the fits print( fit2sls ) print( fit3sls ) print( "covariance of residuals used for estimation (from 2sls)" ) print( fit3sls\$residCovEst ) print( "covariance of residuals" ) print( fit3sls\$residCov ) ## examine the improvement of 3SLS over 2SLS by computing ## the ratio of the standard errors of the estimates improve.ratio <- se.ratio.systemfit( fit2sls, fit3sls, 2 ) print( "summary values for the ratio in the std. err. for the predictions" ) print( summary( improve.ratio ) ) ```