The Log Likelihood (LogL) Object

EViews contains customized procedures which help solve a wide range of the estimation problems that you might encounter. On occasion, however, you may come across an estimation specification which is not included among these specialized routines. This specification may be an extension of an existing procedure, or it could be an entirely new class of problem.

Fortunately, EViews provides you with tools to estimate a wide variety of specifications through the log likelihood (logl) object. The logl object provides you with a general, open-ended tool for estimating a broad class of specifications by maximizing a likelihood function with respect to parameters.

When working with a log likelihood object, you will use EViews’ series generation capabilities to describe the log likelihood contribution of each observation in your sample as a function of unknown parameters. You may supply analytical derivatives of the likelihood for one or more parameters, or you can simply let EViews calculate numeric derivatives automatically. EViews will search for the parameter values that maximize the specified likelihood function, and will provide estimated standard errors for these parameter estimates.

You should note that while useful in a wide range of settings, the Logl object is nevertheless restricted in the types of functions that it can handle. In particular, the Logl requires that all computations be specified using series expressions, and that the log-likelihood objective can be expressed as a series containing log-likelihood contributions for each observation. For more general optimization problems, you should consider the optimize command (see “User-Defined Optimization”).

In this section, we provide an overview and describe the general features of the logl object. We also give examples of specifications which may be estimated using the object. The examples include: multinomial logit, unconditional maximum likelihood AR(1) estimation, Box-Cox regression, disequilibrium switching models, least squares with multiplicative heteroskedasticity, probit specifications with heteroskedasticity, probit with grouped data, nested logit, zero-altered Poisson models, Heckman sample selection models, Weibull hazard models, GARCH(1,1) with t-distributed errors, GARCH with coefficient restrictions, EGARCH with a generalized error distribution, and multivariate GARCH.