TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 178

SAS/Ets User's Guide 178. 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 | 1762 F Chapter 27 The SYSLIN Procedure Computational Details. 1799 Missing Values. 1802 OUT Data Set. 1803 OUTEST Data Set. 1803 OUTSSCP Data Set . 1804 Printed Output. 1805 ODS Table ODS Graphics. 1808 Examples SYSLIN Procedure. 1808 Example Klein s Model I Estimated with LIML and 3SLS. 1808 Example Grunfeld s Model Estimated with SUR. 1816 Example Illustration of ODS Graphics. 1819 References . 1823 Overview SYSLIN Procedure The SYSLIN procedure estimates parameters in an interdependent system of linear regression equations. Ordinary least squares OLS estimates are biased and inconsistent when current period endogenous variables appear as regressors in other equations in the system. The errors of a set of related regression equations are often correlated and the efficiency of the estimates can be improved by taking these correlations into account. The SYSLIN procedure provides several techniques that produce consistent and asymptotically efficient estimates for systems of regression equations. The SYSLIN procedure provides the following estimation methods ordinary least squares OLS two-stage least squares 2SLS limited information maximum likelihood LIML K-class seemingly unrelated regressions SUR iterated seemingly unrelated regressions ITSUR three-stage least squares 3SLS iterated three-stage least squares IT3SLS full information maximum likelihood FIML minimum expected loss MELO Getting Started SYSLIN Procedure F 1763 Other features of the SYSLIN procedure enable you to impose linear restrictions on the parameter estimates test linear hypotheses about the parameters write predicted and residual values to an output SAS data set write parameter estimates to an output SAS data set write the crossproducts matrix SSCP to an output SAS data set use raw data correlations covariances or cross products as input Getting Started SYSLIN Procedure This section introduces the use of the SYSLIN procedure. The problem of dependent regressors is .

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