The TSCSREG Procedure |
MODEL Statement |
The MODEL statement specifies the regression model and the error structure assumed for the regression residuals. The response variable on the left side of the equal sign is regressed on the independent variables listed after the equal sign. Any number of MODEL statements can be used. For each model statement, only one response variable can be specified on the left side of the equal sign.
The error structure is specified by the FIXONE, FIXTWO, RANONE, RANTWO, FULLER, PARKS, and DASILVA options. More than one of these options can be used, in which case the analysis is repeated for each error structure model specified.
Models can be given labels up to 32 characters in length. Model labels are used in the printed output to identify the results for different models. If no label is specified, the response variable name is used as the label for the model. The model label is specified as follows:
label:
The following options can be specified on the MODEL statement after a slash (/).
prints the matrix of estimated correlations between the parameter estimates.
prints the matrix of estimated covariances between the parameter estimates.
specifies that a one-way fixed-effects model be estimated with the one-way model that corresponds to group effects only.
specifies that the model be estimated by using the Fuller-Battese method, which assumes a variance components model for the error structure.
specifies that the model be estimated by using the Parks method, which assumes a first-order autoregressive model for the error structure.
specifies that the model be estimated by using the Da Silva method, which assumes a mixed variance-component moving-average model for the error structure.
specifies the order of the moving-average process in the Da Silva method. The M= value must be less than . The default is M=1.
prints the matrix of estimated covariances of the observations for the Parks method. The PHI option is relevant only when the PARKS option is used.
prints the estimated autocorrelation coefficients for the Parks method.
specifies a singularity criterion for the inversion of the matrix. The default depends on the precision of the computer system.
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