TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 151

SAS/Ets User's Guide 151. 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 | 1492 F Chapter 22 The SEVERITY Procedure Experimental Overview SEVERITY Procedure The SEVERITY procedure estimates parameters of any arbitrary continuous probability distribution that is used to model magnitude severity of a continuous-valued event of interest. Some examples of such events are loss amounts paid by an insurance company and demand of a product as depicted by its sales. PROC SEVERITY is especially useful when the severity of an event does not follow typical distributions such as the normal distribution that are often assumed by standard statistical methods. PROC SEVERITY provides a default set of probability distribution models that includes the Burr exponential gamma generalized Pareto inverse Gaussian Wald lognormal Pareto and Weibull distributions. In the simplest form you can estimate the parameters of any of these distributions by using a list of severity values that are recorded in a SAS data set. The values can optionally be grouped by a set of BY variables. PROC SEVERITY computes the estimates of the model parameters their standard errors and their covariance structure by using the maximum likelihood method for each of the BY groups. PROC SEVERITY can fit multiple distributions at the same time and choose the best distribution according to a specified selection criterion. Seven different statistics of fit can be used as selection criteria. They are log likelihood Akaike s information criterion AIC corrected Akaike s information criterion AICC Schwarz Bayesian information criterion BIC Kolmogorov-Smirnov statistic KS Anderson-Darling statistic AD and Cramer-von-Mises statistic CvM . You can request the procedure to output the status of the estimation process the parameter estimates and their standard errors the estimated covariance structure of the parameters the statistics of fit estimated cumulative distribution function CDF for each of the specified distributions and the empirical distribution function EDF estimate which is used to compute .

• 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