Object Reference : Object View and Procedure Reference : Equation
Estimates models where the dependent variable is a nonnegative integer count.
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.
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),““cr” (cluster robust).
covinfo = arg
Information matrix method: “opg” (OPG); “hessian” (observed Hessian).
(Applicable when non-legacy “optmethod=”.)
Degree-of-freedom correct the coefficient covariance estimate.(For non-cluster robust methods estimated using non-legacy estimation).
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”).
crtype=arg (default “cr1”)
Cluster robust weighting method: “cr0” (no finite sample correction), “cr1” (finite sample correction), when “cov=cr”.
Cluster robust series name, when “cov=cr”.
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:
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.
See “Count Models” for additional discussion.