count Equation Methods
Estimates models where the dependent variable is a nonnegative integer count.
Syntax
eq_name.count(options) y x1 [x2 x3...]
eq_name.count(options) specification
Follow the count keyword by the name of the dependent variable and a list of regressors or provide a linear specification.
Options

 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. cov=arg 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=”.) 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 the EViews default values (out of range values are set to “s=1”). prompt Force the dialog to appear from within a program. p Print the result.
Examples
The command:
equation eq1.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.
equation eq1.count arrest c job police
eq1.makeresids(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.
equation eq1.count(d=p) y c x1
eq1.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.
equation eq1.count(d=p, h) y c x1
estimates the same model with QML standard errors and covariances.
Cross-references