TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 102

SAS/Ets User's Guide 102. 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 | 1002 F Chapter 18 The MODEL Procedure Figure Summary of Residual Errors Report The MODEL Procedure Nonlinear OLS Summary of Residual Errors DF DF Adj Equation Model Error SSE MSE R-Square R-Sq LHUR 3 141 This table lists the sum of squared errors SSE the mean squared error MSE the root mean squared error root MSE and the R2 and adjusted R2 statistics. The R2 value of means that the estimated model explains approximately 75 percent more of the variability in LHUR than a mean model explains. Following the summary of residual errors is the parameter estimates table shown in Figure . Figure Parameter Estimates Parameter Nonlinear OLS Parameter Estimates Approx Pr t Estimate Approx Std Err t Value a b c .0001 Because the model is nonlinear the standard error of the estimate the t value and its significance level are only approximate. These values are computed using asymptotic formulas that are correct for large sample sizes but only approximately correct for smaller samples. Thus you should use caution in interpreting these statistics for nonlinear models especially for small sample sizes. For linear models these results are exact and are the same as standard linear regression. The last part of the output produced by the FIT statement is shown in Figure . Figure System Summary Statistics Number of Observations Statistics for System Used 144 Objective Missing 1 Objective N This table lists the objective value for the estimation of the nonlinear system. Since there is only a single equation in this case the objective value is the same as the residual MSE for LHUR except that the objective value does not include a degrees-of-freedom correction. This can be seen in the fact that Objective N equals the residual SSE . N is 144 the number of observations used. Nonlinear Systems Regression F 1003 Convergence and Starting

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• Hướng dẫn sử dụng SAS ETS
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