Object Reference : Object View and Procedure Reference : Equation
Estimate binary dependent variable models.
Estimates models where the binary dependent variable Y is either zero or one (probit, logit, gompit).
eq_name.binary(options) y x1 [x2 x3 ...]
eq_name.binary(options) specification
d=arg (default=“n”)
Specify likelihood: normal likelihood function, probit (“n”), logistic likelihood function, logit (“l”), Type I extreme value likelihood function, Gompit (“x”).
optmethod = arg
Optimization method: “bfgs” (BFGS); “newton” (Newton-Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy).
Newton-Raphson is the default method.
optstep = arg
Step method: “marquardt” (Marquardt); “dogleg” (Dogleg); “linesearch” (Line search).
Marquardt is the default method.
Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method), “glm” (GLM method).
covinfo = arg
Information matrix method: “opg” (OPG); “hessian” (observed Hessian - default).
(Applicable when non-legacy “optmethod=”.)
Huber-White quasi-maximum likelihood (QML) standard errors and covariances.
(Legacy option applicable when “optmethod=legacy”).
GLM standard errors and covariances.
(Legacy option applicable when “optmethod=legacy”).
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.
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.
Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector.
Force the dialog to appear from within a program.
Print results.
To estimate a logit model of Y using a constant, WAGE, EDU, and KIDS, and computing Huber-White standard errors, you may use the command:
equation eq1.binary(d=l,cov=huber) y c wage edu kids
Note that this estimation uses the default global optimization options. The commands:
param c(1) .1 c(2) .1 c(3) .1
equation probit1.binary(s) y c x2 x3
estimate a probit model of Y on a constant, X2, and X3, using the specified starting values. The commands:
coef beta_probit = probit1.@coefs
matrix cov_probit = probit1.@coefcov
store the estimated coefficients and coefficient covariances in the coefficient vector BETA_PROBIT and matrix COV_PROBIT.
See “Binary Dependent Variable Models” for additional discussion.