TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 206

SAS/Ets User's Guide 206. 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 | 2042 F Chapter 31 The UCM Procedure if the model is stationary or if the model is nonstationary and there are no missing values in the data. See Chapter 7 The ARIMA Procedure for additional details about the ARIMA procedure. However if there are missing values in the data and the model is nonstationary then the UCM and ARIMA procedures can produce significantly different parameter estimates and predictions. An article by Kohn and Ansley 1986 suggests a statistically sound method of estimation prediction and interpolation for nonstationary ARIMA models with missing data. This method is based on an algorithm that is equivalent to the Kalman filtering and smoothing algorithm used in the UCM procedure. The results of an illustrative example in their article are reproduced here using the UCM procedure. In this example an ARIMA 0 1 1 x 0 1 1 12 model is applied to the logarithm of the air series in the data set. Four different missing value patterns are considered to highlight different aspects of the problem Datal. The full data set of 144 observations. Data2. The set of 78 observations that omit January through November in each of the last 6 years. Data3. The data set with the 5 observations July 1949 June July and August 1957 and July 1960 missing. Data4. The data set with all July observations missing and June and August 1957 also missing. The following DATA steps create these data sets data Datal set logair log air run data Data2 set datal if year date 1955 and month date 12 then logair . run data Data3 set data1 if year date 1949 and month date 7 then logair . if year date 1957 and month date 6 or month date 7 or month date 8 then logair . if year date 1960 and month date 7 then logair . run data Data4 set data1 if month date 7 then logair . if year date 1957 and month date 6 or month date 8 then logair . run The following statements specify the ARIMA 0 1 1 x 0 1 1 12 model for the logair series in the first data set Datal Example ARIMA

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