TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 55

SAS/Ets User's Guide 55. Provides detailed reference material for using SAS/ETS software and guides you through the analysis and forecasting of features such as univariate and multivariate time series, cross-sectional time series, seasonal adjustments, multiequational nonlinear models, discrete choice models, limited dependent variable models, portfolio analysis, and generation of financial reports, with introductory and advanced examples for each procedure. You can also find complete information about two easy-to-use point-and-click applications: the Time Series Forecasting System, for automatic and interactive time series modeling and forecasting, and the Investment Analysis System, for time-value of money analysis of a variety of investments | 532 F Chapter 10 The COUNTREG Procedure Nested regressors are specified by following a dummy variable or dummy interaction with a classification variable or list of classification variables enclosed in parentheses. The dummy variable or dummy interaction is nested within the regressor listed in parentheses B A C B A D E C B A . In this example B A is read B nested within A. Continuous-by-class regressors are written by joining continuous variables and classification variables with asterisks X1 A. Continuous-nesting-class regressors consist of continuous variables followed by a classification variable interaction enclosed in parentheses X1 A X1 X2 A B . One example of the general form of an effect that involves several variables is X1 X2 A B C D E This example contains interacting continuous terms with classification terms that are nested within more than one classification variable. The continuous list comes first followed by the dummy list followed by the nesting list in parentheses. Note that asterisks can appear within the nested list but not immediately before the left parenthesis. The MODEL statement and several other statements use these effects. Some examples of MODEL statements that use various kinds of effects are shown in the following table where a b and c represent classification variables and y y1 y2 x and z represent continuous variables. Specification Type of Model model y x Simple regression model y x z Multiple regression model y x x x Polynomial regression model y a Regression with one classification variable model y a b c Regression with multiple classification variables model y a b a b Regression with classification variables and their interactions model y a b a c b a Regression with classification variables and their interactions model y a x Regression with both countibuous and classification variables model y a x a Reparate-slopes regression model y a x x a Homogeneity-of-slopes regression The Bar Operator You can shorten the specification of

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