TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 192

SAS/Ets User's Guide 192. 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 | 1902 F Chapter 29 The TIMESERIES Procedure After each set of transactions has been accumulated to form corresponding time series accumulated time series can be analyzed using various time series analysis techniques. For example exponentially weighted moving averages can be used to smooth each series. The following statements use the EXPAND procedure to smooth the analysis variable named STOREITEM. proc expand data mseries out smoothed from month by store id date convert storeitem smooth transform ewma run The smoothed series are stored in the data set . The variable SMOOTH contains the smoothed series. If the time ID variable TIMESTAMP contains SAS datetime values instead of SAS date values the INTERVAL START and END options must be changed accordingly and the following statements could be used proc timeseries data retail out tseries by store id timestamp interval dtmonth accumulate median setmiss 0 start 01jan1998 00 00 00 dt end 31dec2000 00 00 00 dt var _numeric_ run The monthly time series data are stored in the data and the time ID values use a SAS datetime representation. Example Trend and Seasonal Analysis This example illustrates using the TIMESERIES procedure for trend and seasonal analysis of time-stamped transactional data. Suppose that the data set SASHELPAIR contains two variables DATE and AIR. The variable DATE contains sorted SAS date values recorded at no particular frequency. The variable AIR contains the transaction values to be analyzed. The following statements accumulate the transactional data on an average basis to form a quarterly time series and perform trend and seasonal analysis on the transactions. proc timeseries data out series outtrend trend outseason season print seasons id date interval qtr accumulate avg var air run Example Trend and Seasonal Analysis F 1903 The time series is stored in the data set the trend statistics are stored in the data set and 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