SAS/ETS Software
Data Manipulation
Time Series Interpolation and Frequency Conversion
The EXPAND procedure converts time series from one sampling interval or frequency to another and interpolates missing values in time series. PROC EXPAND enables you to change time series data from higher frequency intervals to lower frequency intervals, or vice versa. You can also interpolate missing values in a time series, either without changing series frequency or in conjunction with expanding or collapsing the series. The procedure automatically accounts for calendar effects such as the differing number of days in each month and leap years.
The EXPAND procedure also handles conversions of frequencies that cannot be defined by standard interval names. For example, if you specify the option FACTOR=(13:2), 13 equally spaced output observations are interpolated for each pair of input observations.
You can also convert an aperiodic series, observed at arbitrary points in time, into periodic estimates. For example, a series of randomly timed quality control spot-check results might be interpolated to form estimates of monthly average defect rates.
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| Use the EXPAND procedure to interpolate for missing values as well as change the time series frequency. |
Details of the EXPAND Procedure
Seasonal Adjustment Using X-11 and X-11 ARIMA Methods
The X11 procedure is based on the U.S. Bureau of the Census X-11 seasonal adjustment program. The procedure seasonally adjusts monthly or quarterly time series, makes additive or multiplicative adjustments, and creates an output data set containing the adjusted time series and intermediate calculations.
The X11 procedure also provides the X11-ARIMA method developed by Statistics Canada. This method fits an ARIMA model to the original series and then uses the model forecast to extend the original series. This extended series is then seasonally adjusted by the standard X11 seasonal adjustment method. The extension of the series improves the estimation of the seasonal factors and reduces revisions to the seasonally adjusted series as new data becomes available.
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| Use the X11 procedure to seasonally adjust monthly or quarterly time series. |
The X11 procedure is used to decompose seasonal monthly or quarterly series into seasonal (S), trend cycle (C), trading-day (D), and irregular (I) components. A seasonally adjusted time series consists of the trend cycle and irregular components. The procedure processes any number of variables at once with no maximum length for a series. It projects the seasonal component one year ahead, allowing reintroduction of seasonal factors for an extrapolated series.
The naming convention used in the X11 procedure for the tables follows the original U.S. Bureau of the Census X-11 Seasonal Adjustment program specification. You can optionally print or store, in SAS data sets, the individual X11 tables showing the various components at different stages of the computation.
Sliding spans analysis is done by the X11 procedure to qualify the stability of the seasonal adjustment process and, hence, quantify the suitability of seasonal adjustment for a given series. Statistical tests for stable seasonal patterns, and for moving and combined seasonality, are computed and printed following table D8.
Details of the X11 Procedure
Seasonal Adjustment Using X-12 and X-12 ARIMA Methods
The X12 procedure, an adaptation of the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program, is now available with SAS9. The X-12-ARIMA program combines the capabilities of the X-11 program and the X-11-ARIMA/88 program and also introduces some new feathers. One of the main enhancements involves the use of a regARIMA model, a regression model with ARIMA (autoregressive integrated moving average) errors. Thus, the X-12-ARIMA program contains methods developed by both the U.S. Census Bureau and Statistics Canada.
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| Use the X12 procedure to seasonally adjust monthly or quarterly time series. |
Details of the X12 Procedure
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