800—Chapter 26. Models

•A line beginning with “@ADD” specifies an add factor. The add factor command has the form:

@add(v) endogenous_name add_name

where endogenous_name is the name of the endogenous variable of the equation to which the add factor will be applied, and add_name is the name of the series. The option (v) is used to specify that the add factor should be applied to the endogenous variable. The default is to apply the add factor to the residual of the equation. See “Using Add Factors” on page 802 for details.

•A line beginning with “@INNOV”' specifies an innovation variance. The innovation variance has two forms. When applied to an endogenous variable it has the form:

@innov endogenous_name number

where endogenous name is the name of the endogenous variable and number is the standard deviation of the innovation to be applied during stochastic simulation. When applied to an exogenous variable, it has the form:

@innov exogenous_name number_or_series

where exogenous name is the name of the exogenous variable and number_or_series is either a number or the name of the series that contains the standard deviation to be applied to the variable during stochastic simulation. Note that when an equation in a model is linked to an external estimation object, the variance from the estimated equation will be brought into the model automatically and does not require an @innov specification unless you would like to modify its value.

•The keyword “@TRACE”, followed by the names of the endogenous variables that you wish to trace, may be used to request model solution diagnostics. See “Diagnostics” on page 813.

Users of earlier versions of EViews should note that two commands that were previously available, @assign and @exclude, are no longer part of the text form of the model. These commands have been removed because they now address options that apply only to specific model scenarios rather than to the model as a whole. When loading in models created by earlier versions of EViews, these commands will be converted automatically into scenario options in the new model object.

Specifying Scenarios

When working with a model, you will often want to compare model predictions under a variety of different assumptions regarding the paths of your exogenous variables, or with one or more of your equations excluded from the model. Model scenarios allow you to do this without overwriting previous data or changing the structure of your model.

802—Chapter 26. Models

to the baseline data, which involves copying the series associated with any overridden variables in the selected scenario on top of the baseline values. Applying a scenario to the baseline is a way of committing to the edited values of the selected scenario making them a permanent part of the baseline case.

The Scenario overrides page provides a summary of variables which have been overridden in the selected scenario and equations which have been excluded. This is a useful way of seeing a complete list of all the changes which have been made to the scenario from the baseline case.

The Aliasing page allows you to examine the name aliasing rules associated with any scenario. The page displays the complete set of aliases that will be applied to the different types of variables in the model.

Although the scenario dialog lets you see all the settings for a scenario in one place, you will probably alter most scenario settings directly from the variable view instead. For both exogenous variables and add factors, you can select the variable from the variable view window, then use the right mouse button menu to call up the properties page for the variable. The override status of the variable can be adjusted using the Use override checkbox. Once a variable has been overridden, it will appear in red in the variable view.

Using Add Factors

Normally, when a model is solved deterministically, the equations of the model are solved so that each of the equations of the model is exactly satisfied. When a model is solved stochastically, random errors are added to each equation, but the random errors are still chosen so that their average value is zero.

If we have no information as to the errors in our stochastic equations that are likely to occur during the forecast period, then this behavior is appropriate. If, however, we have additional information as to the sort of errors that are likely during our forecast period, then we may incorporate that information into the model using add factors.

The most common use for add factors is to provide a smoother transition from historical data into the forecast period. Typically, add factors will be used to compensate for a poor fit of one or more equations of the model near the end of the historical data, when we suspect this will persist into the forecast period. Add factors provide an ad hoc way of trying to adjust the results of the model without respecifying or reestimating the equations of the model.

In reality, an add factor is just an extra exogenous variable which is included in the selected equation in a particular way. EViews allows an add factor to take one of two forms. If our equation has the form: