Object Reference : Object View and Procedure Reference : Var
Estimate a switching VAR specification.
var_name.switchvar(options) lag_pairs endog_list [@ exog_list] [ @prv list_of_probability_regressors ]
You must specify the order of the VAR (using one or more pairs of lag intervals), and then provide a list of series or groups to be used as endogenous variables. You may include exogenous variables such as trends and seasonal dummies in the VAR by including an “@-sign” followed by a list of series or groups. A constant is automatically added to the list of exogenous variables; to estimate a specification without a constant, you should use the option “noconst”.
General options
Switching mean specification (default is switching intercept).
Exogenous variables are non-regime specific (default is for exogenous variables to vary).
Lagged endogenous variables are regime specific (default is for the endogenous variables to be non-varying).
Do not include a constant in exogenous regressors list.
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.
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”).
In addition to the specification options, there are options for estimation and covariance calculation.
Additional 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.
Set maximum number of iterations.
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.
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=”.)
Do not degree-of-freedom correct the coefficient covariance estimate.
Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector.
Use the current coefficient values in “C” as starting values (see also param).
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.
Force the dialog to appear from within a program.
Print results.
var01.switchvar(type=markov) 1 3 m1 gdp
declares and estimates a MSI(2)-VAR(3) with two endogenous variables (M1 and GDP) and a switching constant.
var01.switchvar(type=markov, msm) 1 3 m1 gdp
estimates the MSM(2)-VAR(3) variant of the same specification..
var01.switchvar(type=markov, msm, endogvary, seed=1551063419) 1 3 m1 gdp
estimates a MSMA(2)-VAR(3) specification.
See “Switching VAR” for details.
See also Var::ls and Var::ec for estimation of ordinary VARs and error correction models.