Object Reference : Object View and Procedure Reference : Equation
  
 
count
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
See “Count Models” for additional discussion.