TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 81

SAS/Ets User's Guide 81. 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 | 792 F Chapter 14 The EXPAND Procedure For weighted moving time window operators the weights for the unavailable or unused observations are ignored and the remaining weights renormalized to sum to 1. Cumulative Statistics Operators Some operators compute cumulative statistics for a set of current and previous values of the series. The cumulative statistics operators are CUAVE CUCSS CUMAX CUMED CUMIN CURANGE CUSTD CUSUM CUUSS and CUVAR. By default the cumulative statistics operators compute the statistics from all previous values of the series so that yt is based on the set of values xt xt-i . xi. For example the following statement computes yt as the cumulative sum of nonmissing Xi values for i t. convert x y transformout cusum You can specify a lag increment argument n for the cumulative statistics operators. In this case the statistic is computed from the current and every nth previous value. When n is specified these operators compute statistics of the values xt xt-n xt-2n . xt-in for t in 0. For example the following statement computes yt as the cumulative sum of nonmissing xi values for odd i when t is odd and for even i when t is even. convert x y transformout cusum 2 The results of this example are yi xi y2 x2 y3 xi x3 y 4 x2 x4 y5 xi x3 xs y 6 x2 x4 x6 Missing Values You can truncate the length of the result series by using the TRIM TRIMLEFT and TRIMRIGHT operators to set values to missing at the beginning or end of the series. You can use these functions to trim the results of moving time window operators so that the result series contains only values computed from a full width time window. For example the following statements compute a centered five-period moving average of X and they set to missing values at the ends of the series that are averages of fewer than five values. Transformation Operations F 793 convert x y transformout cmovave 5 trim 2 Normally the moving time window and cumulative statistics operators ignore missing values and compute their .

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