TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 13

SAS/Ets User's Guide 13. 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 | 112 F Chapter 3 Working with Time Series Data if date A . then output temp2 run data uscpi merge uscpi tempi temp2 by date run Summing Series Simple cumulative sums are easy to compute using SAS sum statements. The following statements show how to compute the running sum of variable X in data set A adding XSUM to the data set. data a set a xsum x run The SAS sum statement automatically retains the variable XSUM and initializes it to 0 and the sum statement treats missing values as 0. The sum statement is equivalent to using a RETAIN statement and the SUM function. The previous example could also be written as follows data a set a retain xsum xsum sum xsum x run You can also use the EXPAND procedure to compute summations. For example proc expand data a out a method none convert x xsum transform sum run Like differencing summation can be done at different lags and can be repeated to produce higher-order sums. To compute sums over observations separated by lags greater than 1 use the LAG and SUM functions together and use a RETAIN statement that initializes the summation variable to zero. For example the following statements add the variable XSUM2 to data set A. XSUM2 contains the sum of every other observation with even-numbered observations containing a cumulative sum of values of X from even observations and odd-numbered observations containing a cumulative sum of values of X from odd observations. data a set a Transforming Time Series F 113 retain xsum2 0 xsum2 sum lag xsum2 x run Assuming that A is a quarterly data set the following statements compute running sums of X for each quarter. XSUM4 contains the cumulative sum of X for all observations for the same quarter as the current quarter. Thus for a first-quarter observation XSUM4 contains a cumulative sum of current and past first-quarter values. data a set a retain xsum4 0 xsum4 sum lag3 xsum4 x run To compute higher-order sums repeat the preceding process and sum the summation variable. For example the .

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