surerob {robustsur}

Robust estimation for Seemingly Unrelated Regression Models

Description

Robust estimation for Seemingly Unrelated Regression Models in presence of cell-wise and case-wise outliers performed using a three-stage procedure. In the first step estimation of the coefficients in each single-equation model is obtained using a Robust Regression procedure, robust estimation of the residual covariance is obtained by a Two-Step Generalized S-estimator, a weighted least square is performed on the whole system to get final estimates of the regression coefficients.

Usage

surerob(formula, data, control=lmrob.control(), ...)
## S3 method for class 'surerob'
print(x, digits=max(3, getOption("digits")-1), ...)  

Arguments

formula

a list of objects of class formula for multiple-equation models; for single-equation models use function lmrob.

data

a list of objects of class data.frame. Each data.frame contains the data for the corresponding model and all the data.frames must have the same number of observations.

control

list of control parameters. The default is constructed by the function lmrob.control, and it is passed to function lmrob.

...

arguments passed to the function TSGS.

x

an object of class surerob.

digits

number of digits to print.

Details

The estimation of systems of equations with unequal numbers of observations is not implemented.

Value

surerob returns a list of the class surerob and contains all results that belong to the whole system. This list contains one special object: "eq". It is a list and contains one object for each estimated equation. These objects are of the class lmrob and contain the results that belong only to the regarding equation.

The objects of the class surerob have the following components:

eq

a list that contains the results that belong to the individual equations.

call

the matched call.

method

estimation method.

rank

total number of linear independent coefficients.

coefficients

vector of all estimated coefficients.

fitted.values

matrix of fitted values.

residuals

matrix of residuals

imp.residuals

imputed residuals from TSGS.

residCovEst

residual covariance matrix used for estimation.

residCov

estimated residual covariance matrix.

rweights

matrix of robust weights.

TSGS

object from function TSGS.

control

list of control parameters used for the estimation.

df.residual

degrees of freedom of the whole system.

y

response observations used in the second step.

x

design matrix used in the second step.

Author(s)

Claudio Agostinelli and Giovanni Saraceno

References

Giovanni Saraceno, Fatemah Alqallaf and Claudio Agostinelli (2021?) A Robust Seemingly Unrelated Regressions For Row-Wise And Cell-Wise Contamination, submitted

See Also

lmrob, lm and systemfit

Examples

  library(systemfit)
  data("Kmenta")
  eqDemand <- consump~price+income
  eqSupply <- consump~price+farmPrice+trend
  system <- list(demand=eqDemand, supply=eqSupply)
     
  ## Robust estimation
  fitrob <- surerob(system, data=Kmenta)
  print(fitrob)