Command Reference : Command Reference
Estimates models where the dependent variable is a nonnegative integer count.
count(options) y x1 [x2 x3...]
count(options) specification
Follow the count keyword by the name of the dependent variable and a list of regressors.
d=arg (default=“p”)
Likelihood specification: Poisson likelihood (“p”), normal quasi-likelihood (“n”), exponential likelihood (“e”), negative binomial likelihood or quasi-likelihood (“b”).
v=positive_num (default=1)
Specify fixed value for QML parameter in normal and negative binomial quasi-likelihoods.
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 methods)., “glm” (GLM method)..
covinfo = arg
Information matrix method: “opg” (OPG); “hessian” (observed Hessian).
(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 the EViews default values (out of range values are set to “s=1”).
Force the dialog to appear from within a program.
Print the result.
The command:
count(d=n,v=2,cov=glm) y c x1 x2
estimates a normal QML count model of Y on a constant, X1, and X2, with fixed variance parameter 2, and GLM standard errors.
count arrest c job police
makeresid(g) res_g
estimates a Poisson count model of ARREST on a constant, JOB, and POLICE, and stores the generalized residuals in the series RES_G.
count(d=p) y c x1
fit yhat
estimates a Poisson count model of Y on a constant and X1, and saves the fitted values (conditional mean) in the series YHAT.
count(d=p, h) y c x1
estimates the same model with QML standard errors and covariances.
See “Count Models” for additional discussion.
See Equation::count for the equivalent equation object command.