Object Reference : Object View and Procedure Reference : Sspace
  
 
ml
Maximum likelihood estimation of state space models.
Syntax
sspace_name.ml(options)
Options
 
optmethod = arg
Optimization method: “bfgs” (BFGS); “newton” (Newton-Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy).
BFGS is the default method.
optstep = arg
Step method: “marquardt” (Marquardt); “dogleg” (Dogleg); “linesearch” (Line search).
Marquardt is the default method.
cov=arg
Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich methods).,
covinfo = arg
Information matrix method: “opg” (OPG); “hessian” (observed Hessian).
(Applicable when non-legacy “optmethod=”.)
b
Use Berndt-Hall-Hall-Hausman (BHHH) algorithm (default is Marquardt).
m=integer
Set maximum number of iterations.
c=scalar
Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coefficients.
showopts / ‑showopts
[Do / do not] display the starting coefficient values and estimation options in the estimation output.
prompt
Force the dialog to appear from within a program.
p
Print basic estimation results.
Examples
bvar.ml
estimates the sspace object BVAR by maximum likelihood.
Cross-references
See “State Space Models and the Kalman Filter” for a discussion of user specified state space models.