TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 78

SAS/Ets User's Guide 78. 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 | 762 Chapter 14 The EXPAND Procedure Contents Overview EXPAND Procedure. 764 Getting Started EXPAND Procedure . 765 Converting to Higher Frequency Series. 765 Aggregating to Lower Frequency Series. 765 Combining Time Series with Different Frequencies. 766 Interpolating Missing Values. 767 Requesting Different Interpolation Methods. 767 Using the ID Statement . 768 Specifying Observation Characteristics. 768 Converting Observation Characteristics. 769 Creating New Variables. 770 Transforming Series. 770 Syntax EXPAND Procedure. 772 Functional Summary. 772 PROC EXPAND Statement. 773 BY Statement . 775 CONVERT Statement. 776 ID Statement. 777 Details EXPAND Procedure. 778 Frequency Conversion. 778 Identifying Observations. 779 Range of Output Observations. 780 Extrapolation. 781 OBSERVED Option. 781 Conversion Methods. 783 Transformation Operations. 786 OUT Data Set. 801 OUTEST Data Set. 801 ODS Graphics. 802 Examples EXPAND Procedure. 804 Example Combining Monthly and Quarterly Data. 804 Example Illustration of ODS Graphics. 807 Example Interpolating Irregular Observations . 811 Example Using Transformations . 814 References . 815 764 F Chapter 14 The EXPAND Procedure Overview EXPAND Procedure The EXPAND procedure converts time series from one sampling interval or frequency to another and interpolates missing values in time series. A wide array of data transformations is also supported. Using PROC EXPAND you can collapse time series data from higher frequency intervals to lower frequency intervals or expand data from lower frequency intervals to higher frequency intervals. For example quarterly values can be aggregated to produce an annual series or quarterly estimates can be interpolated from an annual series. Time series frequency conversion is useful when you need to combine series with different sampling intervals into a single data set. For example if you need as input to a monthly model a series that is only available quarterly you might use

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