TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 14

SAS/Ets User's Guide 14. 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 | 122 F Chapter 3 Working with Time Series Data By default the EXPAND procedure performs interpolation by first fitting cubic spline curves to the available data and then computing needed interpolating values from the fitted spline curves. Other interpolation methods can be requested. Note that interpolating values of a time series does not add any real information to the data because the interpolation process is not the same process that generated the other nonmissing values in the series. While time series interpolation can sometimes be useful great care is needed in analyzing time series that contain interpolated values. Interpolating Missing Values To use the EXPAND procedure to interpolate missing values in a time series specify the input and output data sets in the PROC EXPAND statement and specify the time ID variable in an ID statement. For example the following statements cause PROC EXPAND to interpolate values for missing values of all numeric variables in the data set USPRICE proc expand data usprice out interpl id date run Interpolated values are computed only for embedded missing values in the input time series. Missing values before or after the range of a series are ignored by the EXPAND procedure. In the preceding example PROC EXPAND assumes that all series are measured at points in time given by the value of the ID variable. In fact the series in the USPRICE data set are monthly averages. PROC EXPAND can produce a better interpolation if this is taken into account. The following example uses the FROM MONTH option to tell PROC EXPAND that the series is monthly and uses the CONVERT statement with the OBSERVED AVERAGE to specify that the series values are averages over each month proc expand data usprice out interpl from month id date convert cpi ppi observed average run Interpolating to a Higher or Lower Frequency You can use PROC EXPAND to interpolate values of time series at a higher or lower sampling frequency than the input time series. To change .

• 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