Box–Jenkins method: Difference between revisions

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==Box–Jenkins model estimation==

Estimating the parameters for Box-Jenkins models involves numerically approximating the solutions of nonlinear equations. For this reason, it is common to use statistical software designed to handle to the approach - fortunately, virtually all modern statistical packages feature this capability. The main approaches to fitting Box–Jenkins models are non-linearnonlinear least squares and maximum likelihood estimation. Maximum likelihood estimation is generally the preferred technique. The likelihood equations for the full Box–Jenkins model are complicated and are not included here. See (Brockwell and Davis, 1991) for the mathematical details.

Estimating the parameters for the Box–Jenkins models is a quite complicated non-linear estimation problem. For this reason, the parameter estimation should be left to a high quality software program that fits Box–Jenkins models. Fortunately, many statistical software programs now fit Box–Jenkins models.

The main approaches to fitting Box–Jenkins models are non-linear least squares and maximum likelihood estimation. Maximum likelihood estimation is generally the preferred technique. The likelihood equations for the full Box–Jenkins model are complicated and are not included here. See (Brockwell and Davis, 1991) for the mathematical details.

==Box–Jenkins model diagnostics==