Object Reference : Object View and Procedure Reference : Equation
  
 
binary
Estimate binary dependent variable models.
Estimates models where the binary dependent variable Y is either zero or one (probit, logit, gompit).
Syntax
eq_name.binary(options) y x1 [x2 x3 ...]
eq_name.binary(options) specification
Options
 
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.
cov=arg
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=”.)
h
Huber-White quasi-maximum likelihood (QML) standard errors and covariances.
(Legacy option applicable when “optmethod=legacy”).
g
GLM standard errors and covariances.
(Legacy option applicable when “optmethod=legacy”).
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.
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.
coef=arg
Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector.
prompt
Force the dialog to appear from within a program.
p
Print results.
Examples
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.
Cross-references
See “Binary Dependent Variable Models” for additional discussion.