TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 154

SAS/Ets User's Guide 154. 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 | 1522 F Chapter 22 The SEVERITY Procedure Experimental Sequence and type of arguments x Numeric value of the random variable at which the PDF value should be evaluated pl Numeric value of the first parameter p2 Numeric value of the second parameter . pm Numeric value of the mth parameter Return value Numeric value that contains the PDF value p1 p2 . pm If you want to consider this distribution as a candidate distribution when estimating a response variable model with regression effects then the first parameter of this distribution must be a scale parameter or log-transformed scale parameter. In other words if the distribution has a scale parameter then the following equation must be satisfied P1 P2 . . . Pm f. 1 P2 . Pm P1 P1 If the distribution has a log-transformed scale parameter then the following equation must be satisfied 1x P1 P2 . Pm f. I 0 P2 . Pm Here is a sample structure of the function for a distribution named FOO function FOO_PDF x P1 P2 Code to compute PDF by using x P1 and P2 f computed PDF return f endsub JzsCCDFGRADIENT defines a subroutine that returns the gradient vector of the CDF of the distribution at the specified values of the random variable and distribution parameters. Type Subroutine Required NO Number of arguments m 2 where m is the number of distribution parameters Sequence and type of arguments x Numeric value of the random variable at which the gradient of the CDF should be evaluated pl Numeric value of the first parameter p2 Numeric value of the second parameter . pm Numeric value of the mth parameter Defining a Distribution Model with the FCMP Procedure F 1523 grad Output numeric array of size m that contains the gradient vector evaluated at the specified values. The expected order of the values in the array is as follows. 9F 9F . _3F_ iollows- 3pi dp2 dpm Return value Numeric array that contains the gradient of the CDF evaluated at x for the parameter values p1 p2 pm Here is a sample structure of the .

• Tài chính doanh nghiệp
• Hướng dẫn sử dụng SAS ETS
• các mô hình phân tích tài chính
• báo cáo tài chính
• phân tích đầu tư
• phân tích tính khả dụng các khoản đầu tư