TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 293

SAS/Ets User's Guide 293. 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 | 2912 F Chapter 46 Forecasting Process Details would be written as Dif 2 1 s N 2 1 s . The mathematical notation for the transfer function in this example is Vi B 1 - nAB - B2 1 - s t iB 12 1 - B 2 1 - B12 Note In this case Dif 2 1 s N 2 1 s Dif 2 1 sLag 0 N 2 1 s D 0 0 s . Predictor Series This section discusses time trend curves seasonal dummies interventions and adjustments. Time Trend Curves When you specify a time trend curve as a predictor in a forecasting model the system computes a predictor series that is a deterministic function of time. This variable is then included in the model as a regressor and the trend curve is fit to the dependent series by linear regression in addition to other predictor series. Some kinds of nonlinear trend curves are fit by transforming the dependent series. For example the exponential trend curve is actually a linear time trend fit to the logarithm of the series. For these trend curve specifications the series transformation option is set automatically and you cannot independently control both the time trend curve and transformation option. The computed time trend variable is included in the output data set in a variable named in accordance with the trend curve type. Let t represent the observation count from the start of the period of fit for the model and let Xt represent the value of the time trend variable at observation t within the period of fit. The names and definitions of these variables are as follows. Note These deterministic variables are reserved variable names. Linear trend variable name _LINEAR_ with Xt t c Quadratic trend variable name _QUAD_ with Xt t c 2. Note that a quadratic trend implies a linear trend as a special case and results in two regressors _QUAD_ and _LINEAR_. Cubic trend variable name _CUBE_ with Xt t c 3. Note that a cubic trend implies a quadratic trend as a special case and results in three regressors _CUBE_ _QUAD_ and _LINEAR_. Logistic trend variable name _LOGIT_ with Xt t. The model is a .

• 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