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systemfit (version 1.1-30)

summary.systemfit: Summary of systemfit estimation

Description

These functions create and print summary results of the estimated equation system.

Usage

# S3 method for systemfit
summary( object, useDfSys = NULL,
      residCov = TRUE, equations = TRUE, ... )

# S3 method for systemfit.equation summary( object, useDfSys = NULL, ... )

# S3 method for summary.systemfit print( x, digits = max( 3, getOption("digits") - 1 ), residCov = x$printResidCov, equations = x$printEquations, ... )

# S3 method for summary.systemfit.equation print( x, digits = max( 3, getOption("digits") - 1 ), ... )

Value

Applying summary on an object of class systemfit

returns a list of class summary.systemfit. Applying summary on an object of class

systemfit.equation

returns a list of class

summary.systemfit.equation. An object of class summary.systemfit

contains all results that belong to the whole system. This list contains one special object: eq. This is a list and contains objects of class

summary.systemfit.equation. These objects contain the results that belong to each of the eatimated equations.

The objects of classes summary.systemfit and

summary.systemfit.equation

have the following components (elements that are marked with a \(*\) are available only in objects of class summary.systemfit; elements that are marked with a \(+\) are available only in objects of class summary.systemfit.equation):

method

estimation method.

residuals

residuals.

coefficients

a matrix with columns for the estimated coefficients, their standard errors, t-statistic and corresponding (two-sided) p-values.

df

degrees of freedom, a 2-vector, where the first element is the number of coefficients and the second element is the number of observations minus the number of coefficients.

coefCov

estimated covariance matrix of the coefficients.

call*

the matched call of systemfit.

ols.r.squared*

OLS \(R^2\) value of the entire system.

mcelroy.r.squared*

McElroy's \(R^2\) value for the system.

iter*

number of iteration steps (only if the estimation is iterated).

control*

list of control parameters used for the estimation.

residCov*

estimated residual covariance matrix.

residCovEst*

residual covariance matrix used for estimation (only SUR and 3SLS).

residCor*

correlation matrix of the residuals.

detResidCov*

determinant of residCov.

eqnLabel+

equation label.

eqnNo+

equation number.

terms+

the 'terms' object used for the respective equation.

r.squared+

\(R^2\) value of the respective equation.

adj.r.squared+

adjusted \(R^2\) value of the respective equation.

sigma+

estimated standard error of the residuals of the respective equation.

ssr+

sum of squared residuals of the respective equation.

printResidCov*

argument residCov.

printEquations*

argument equations.

Arguments

object

an object of class systemfit or systemfit.equation.

x

an object of class summary.systemfit or summary.systemfit.equation.

useDfSys

logical. Use the degrees of freedom of the whole system (in place of the degrees of freedom of the single equation) to calculate prob values for the t-test of individual coefficients. If it not specified (NULL), it is set to TRUE if restrictions on the coefficients are imposed and FALSE otherwise.

digits

number of digits to print.

residCov

logical. If TRUE, the residual correlation matrix, the residual covariance matrix, and its determinant are printed.

equations

logical. If TRUE, summary results of each equation are printed. If FALSE, just the coefficients are printed.

...

not used by user.

See Also

systemfit, print.systemfit

Examples

Run this code
data( "Kmenta" )
eqDemand <- consump ~ price + income
eqSupply <- consump ~ price + farmPrice + trend
inst <- ~ income + farmPrice + trend
system <- list( demand = eqDemand, supply = eqSupply )

## perform OLS on each of the equations in the system
fitols <- systemfit( system, data = Kmenta )

## results of the system
summary( fitols )

## short results of the system
summary( fitols, residCov = FALSE, equations = FALSE )

## results of the first equation
summary( fitols$eq[[1]] )

## results of the second equation
summary( fitols$eq[[2]] )

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