method=arg (default=“m”)

Robust estimation method: “m” (M-estimation), “s” (S-estimation) or “mm” (MM-estimation).

cov=arg (default=“type1”)

Covariance method type: “type1”, “type2”, or “type3”.

tuning=number

Specify a value for the tuning parameter. If a value is not specified, EViews will use the default tuning parameter for the type of estimation and weighting function (if applicable).

c=s

Convergence criterion. The criterion will be set to the nearest value between 1e-24 and 0.2.

coef=arg

Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector.

m=integer

Maximum number the number of iterations.

prompt

Force the dialog to appear from within a program.

p

Print results.

fn=arg (default=“bisquare”)

Weighting function used during M-estimation: “andrews” (Andrews), “bisquare” (Bisquare), “cauchy” (Cauchy), “fair”, “huber”, “huberbi” (Huber-bisquare), “logistic” (Logistic), “median”, “tal” (Talworth), “Welsch” (Welsch).

scale=arg (default=“madzero”)

Scaling method used for calculating the scalar parameter during M estimation: “madzero” (median absolute deviation, zero centered), “madmed” (median absolute deviation, median centered), "huber" (Huber scaling).

hmat

Use the hat-matrix to down-weight observations with high leverage.

compare = integer (default=4)

Number of comparison sets.

refine = integer (default= 2)

Number of refinements.

trials = integer (default=200)

Number of trials.

subsmpl=integer

Specifies the size of the subsamples. Note, the default is number of coefficients in the regression.

seed=number

Specifies the random number generator seed

rng=arg

Specifies the type of random number generator. The key can be; improved Knuth generator (“kn”), improved Mersenne Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator used in EViews 4 (“kn4”) L’Ecuyer’s (1999) combined multiple, recursive generator (“le”), Matsumoto and Nishimura’s (1998) Mersenne Twister used in EViews 4 (“mt4”).

mtuning=arg

M-estimator tuning parameter.

Note the S-estimator tuning parameter is set with the “tuning=” option outlined above.

hmat

Use the hat-matrix to down-weight observations with high leverage during m-estimation.