Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
SAS/Ets 9.22 User's Guide 98. 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 | 962 F Chapter 17 The MDC Procedure AIC -2 ln L 2 k SBC 2 ln L ln n k where ln L is the log-likelihood value for the model k is the number of parameters estimated and n is the number of observations that is the number of respondents . Tests on Parameters In general the hypothesis to be tested can be written as Ho h h 0 where h h is an r-by-1 vector-valued function of the parameters h given by the r expressions specified in the TEST statement. Let V be the estimate of the covariance matrix of h. Let h be the unconstrained estimate of h and h be the constrained estimate of h such that h h 0. Let A h dh h dh g Using this notation the test statistics for the three kinds of tests are computed as follows The Wald test statistic is defined as W h h A h VA h 1h h The Wald test is not invariant to reparameterization of the model Gregory and Veall 1985 Gallant 1987 p. 219 . For more information about the theoretical properties of the Wald test see Phillips and Park 1988 . The Lagrange multiplier test statistic is LM k A h V A h k where k is the vector of Lagrange multipliers from the computation of the restricted estimate h. The likelihood ratio test statistic is LR 2 L h - L h where h represents the constrained estimate of h and L is the concentrated log-likelihood value. OUTEST Data Set F 963 For each kind of test under the null hypothesis the test statistic is asymptotically distributed as a X2 random variable with r degrees of freedom where r is the number of expressions in the TEST statement. The p-values reported for the tests are computed from the x2 r distribution and are only asymptotically valid. Monte Carlo simulations suggest that the asymptotic distribution of the Wald test is a poorer approximation to its small sample distribution than that of the other two tests. However the Wald test has the lowest computational cost since it does not require computation of the constrained estimate 9. The following statements are an example of using the TEST statement to .
