EViews Help: switchreg

type=arg	Type of switching: simple exogenous (“simple”), Markov (“markov”).
nstates=integer (default=2)	Number of regimes.
heterr	Allow for heterogeneous error variances across regimes
fprobmat=arg	Name of fixed transition probability matrix allows for fixing specific elements of the time-invariant transition matrix. Leave NAs in elements of the matrix to estimate. The element of the matrix corresponds to .
initprob=arg (default=“ergodic”)	Method for determining initial Markov regime probabilities: ergodic solution (“ergodic”), estimated parameter (“est”), equal probabilities (“uniform”), user-specified probabilities (“user”). If “initprob=user” is specified, you will need to specify the “userinit=” option.
userinit=arg	Name of vector containing user-specified initial Markov probabilities. The vector should have rows equal to the number of states; we expand this to the size of the initial lag state vector where necessary for AR specifications. For use in specifications containing both the “type=markov” and “initprob=user” options.
startnum=arg (default=0 or 25)	Number of random starting values tried. The default is 0 for user-supplied coefficients (option “s”) and 25 in all other cases.
startiter=arg (default=10)	Number of iterations taken after each random start before comparing objective to determine final starting value.
searchnum=arg (default=0)	Number of post-estimation perturbed starting values tried.
searchsds=arg (default=1)	Number of standard deviations to use in perturbed starts (if “searchnum=”) is specified.
seed=positive_integer from 0 to 2,147,483,647	Seed the random number generator. If not specified, EViews will seed random number generator with a single integer draw from the default global random number generator.
rnd=arg (default=“kn” or method previously set using rndseed	Type of random number generator: 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”).

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.
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. The criterion will be set to the nearest value between 1e-24 and 0.2.
cov=arg	Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method).
covinfo = arg	Information matrix method: “opg” (OPG); “hessian” (observed Hessian). (Applicable when non-legacy “optmethod=”.)
nodf	Do not degree-of-freedom correct the coefficient covariance estimate.
coef=arg	Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector.
s	Use the current coefficient values in “C” as starting values (see also param).
s=number	Specify a number between zero and one to determine starting values as a fraction of EViews default values (out of range values are set to “s=1”).
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 results.