References—561

Because the series names are related to the variable names in a consistent way, management tasks can often also be performed from outside the model by using the pattern matching features available in EViews commands (see Appendix A. “Wildcards,” on page 559 of the Command and Programming Reference).

The data management operations from within the model window proceed very similarly to the data display operations. First, select the variables you would like to work with from the variable view, then choose Copy,

Store series…, Fetch series… or Delete series… from the right mouse button menu or the object procedures menu. A dialog will

appear, similar to the one used when making a table or graph.

In the same way as for the table and graph dialogs, the left side of the dialog is used to choose which of the model variables to work with, while the right side of the dialog is used to select one or more series associated with each variable. Most of the choices are exactly the same as for graphs and tables. One significant difference is that the checkboxes for active and comparison scenarios include exogenous variables only if they have been overridden in the scenario. Unlike when displaying or editing the data, if an exogenous variable has not been overridden, the actual series will not be included in its place. The only way to store, fetch or delete any actual series is to use the Actuals checkbox.

After clicking on OK, you will receive the usual prompts for the store, fetch and delete operations. You can proceed as usual.

References

Dennis, J. E. and R. B. Schnabel (1983). “Secant Methods for Systems of Nonlinear Equations,” Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, London.

Jain, Raj and Imrich Chlamtac (1985). “The P2 Algorithm for Dynamic Calculation of Quantiles and Histograms Without Storing Observations,” Communications of the ACM, 28(10), 1076–1085.

Levy, Hanoch and David W. Low (1988). “A Contraction Algorithm for Finding Small Cycle Cutsets,”

Journal of Algorithms, 9, 470-493.

Pindyck, Robert S. and Daniel L. Rubinfeld (1998). Econometric Models and Economic Forecasts, 4th edition, New York: McGraw-Hill.

562—Chapter 34. Models