TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 182

SAS/Ets User's Guide 182. 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 | 1802 F Chapter 27 The SYSLIN Procedure Uncorrelated Errors across Equations The SDIAG option in the PROC SYSLIN statement computes estimates by assuming uncorrelated errors across equations. As a result when the SDIAG option is used the 3SLS estimates are identical to 2SLS estimates and the SUR estimates are the same as the OLS estimates. Overidentification Restrictions The OVERID option in the MODEL statement can be used to test for overidentifying restrictions on parameters of each equation. The null hypothesis is that the predetermined variables that do not appear in any equation have zero coefficients. The alternative hypothesis is that at least one of the assumed zero coefficients is nonzero. The test is approximate and rejects the null hypothesis too frequently for small sample sizes. The formula for the test is given as follows. Let y 3 Y y Z e be the i th equation. Y are the endogenous variables that appear as regressors in the i th equation and Z are the instrumental variables that appear as regressors in the i th equation. Let N be the number of variables in Y and Z . Let v y Y 3 . Let Z represent all instrumental variables T be the total number of observations and K be the total number of instrumental variables. Define l as follows O v0 I - Z Z0 Z 1Z0 v v0 I - Z Z0Z - Z0 v Then the test statistic T - K K - Ni l-1 is distributed approximately as an F with K Ni and T K degrees of freedom. See Basmann 1960 for more information. Fuller s Modification to LIML The ALPHA option in the PROC SYSLIN and MODEL statements parameterizes Fuller s modification to LIML. This modification is k y a n g where a is the value of the ALPHA option y is the LIML k value n is the number of observations and g is the number of predetermined variables. Fuller s modification is not used unless the ALPHA option is specified. See Fuller 1977 for more information. Missing Values Observations that have a missing value for any variable in the analysis are excluded from the computations.

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• Hướng dẫn sử dụng SAS ETS
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