The X12 Procedure 
X11 Statement 
The X11 statement is an optional statement for invoking seasonal adjustment by an enhanced version of the methodology of the Census Bureau X11 and X11Q programs. You can control the type of seasonal adjustment decomposition calculated with the MODE= option. The output includes the final tables and diagnostics for the X11 seasonal adjustment method listed in Table 32.5. Tables C20, D1, and D7 are not displayed by default; you can display these tables by using the TABLES statement.
Table Name 
Description 
B1 
original series, adjusted for prior effects and forecast extended 
C17 
final weights for the irregular component 
C20 
final extreme value adjustment factors 
D1 
modified original data, D iteration 
D7 
preliminary trend cycle, D iteration 
D8 
final unmodified SI ratios (differences) 
D8A 
F tests for stable and moving seasonality, D8 
D9 
final replacement values for extreme SI ratios (differences), D iteration 
D9A 
moving seasonality ratios for each period 
D10 
final seasonal factors 
D10B 
seasonal factors, adjusted for userdefined seasonal 
D10D 
final seasonal difference 
D11 
final seasonally adjusted series 
D11A 
final seasonally adjusted series with forced yearly totals 
D11R 
rounded final seasonally adjusted series (with forced yearly totals) 
D12 
final trend cycle 
D13 
final irregular component 
D16 
combined seasonal and trading day factors 
D16B 
final adjustment differences 
D18 
combined calendar adjustment factors 
E4 
ratio of yearly totals of original and seasonally adjusted series 
E5 
percent changes (differences) in original series 
E6 
percent changes (differences) in seasonally adjusted series 
E6A 
percent changes (differences) in seasonally adjusted series with forced yearly totals (D11.A) 
E6R 
percent changes (differences) in rounded seasonally adjusted series (D11.R) 
E7 
percent changes (differences) in final trend component series 
F2A–F2I 
X11 diagnostic summary 
F3 
monitoring and quality assessment statistics 
F4 
day of the week trading day component factors 
G 
spectral plots 
For more details about the X11 seasonal adjustment diagnostics, see Shiskin, Young, and Musgrave (1967), Lothian and Morry (1978a), and Ladiray and Quenneville (2001).
The following options can appear in the X11 statement.
determines the mode of the seasonal adjustment decomposition to be performed. There are four choices: multiplicative (MODE=MULT), additive (MODE=ADD), pseudoadditive (MODE=PSEUDOADD), and logadditive (MODE=LOGADD) decomposition. If this option is omitted, the procedure performs multiplicative adjustments. Table 32.6 shows the values of the MODE= option and the corresponding models for the original (O) and the seasonally adjusted (SA) series.
Value of Mode Option 
Name 
Model for 
Model for 
MULT 
multiplicative 


ADD 
additive 


PSEUDOADD 
pseudoadditive 


LOGADD 
logadditive 


determines whether forecasts are included in certain tables sent to the output data set. If OUTFORECAST is specified, then forecast values are included in the output data set for tables A6, A7, A8, A9, A10, B1, D10, D10B, D10D, D16, D16B, and D18. The default is not to include forecasts.
specifies which seasonal moving average (also called seasonal "filter") be used to estimate the seasonal factors. These seasonal moving averages are moving averages, meaning that an term simple average is taken of a sequence of consecutive term simple averages. X11DEFAULT is the method used by the U.S. Census Bureau’s X11ARIMA program. The default for PROC X12 is SEASONALMA=MSR, which is the methodology of Statistic Canada’s X11ARIMA/88 program.
Table 32.7 describes the seasonal filter options available for the entire series:
Filter Name 
Description of Filter 
S3X1 
a moving average 
S3X3 
a moving average 
S3X5 
a moving average 
S3X9 
a moving average 
S3X15 
a moving average 
STABLE 
stable seasonal filter. A single seasonal factor for each 
calendar month or quarter is generated by calculating the simple 

average of all the values for each month or quarter (taken after 

detrending and outlier adjustment). 

X11DEFAULT 
a moving average is used to calculate the 
initial seasonal factors in each iteration, and a moving 

average to calculate the final seasonal factors 

MSR 
filter chosen automatically by using the moving seasonality 
ratio of X11ARIMA/88 (Dagum; 1988) 
specifies which Henderson moving average be used to estimate the final trend cycle. Any odd number greater than one and less than or equal to 101 can be specified. Example: TRENDMA=23. If no selection is made, the program selects a trend moving average based on statistical characteristics of the data. For monthly series, a 9, 13, or 23term Henderson moving average is selected. For quarterly series, the program chooses either a 5 or a 7term Henderson moving average.
lists the types of prior adjustment factors, obtained from the regression and outlier statements, that are to be removed from the final seasonally adjusted series. Additive outliers (FINAL=AO), level change and ramp outliers (FINAL=LS), and temporary change (FINAL=TC) can be removed. If this option is not specified, the final seasonally adjusted series contains these effects.
specifies that the seasonally adjusted series be modified to (a) force the yearly totals of the seasonally adjusted series and the original series to be the same (FORCE=TOTALS), (b) adjust the seasonally adjusted values for each calendar year so that the sum of the rounded seasonally adjusted series for any year equals the rounded annual total (FORCE=ROUND), or (c) first force the yearly totals, then round the adjusted series (FORCE=BOTH). When FORCE=TOTALS, the differences between the annual totals is distributed over the seasonally adjusted values in a way that approximately preserves the monthtomonth (or quartertoquarter) movements of the original series. For more details, see Huot (1975) and Cholette (1979). This forcing procedure is not recommended if the seasonal pattern is changing or if trading day adjustment is performed. Forcing the seasonally adjusted totals to be the same as the original series annual totals can degrade the quality of the seasonal adjustment, especially when the seasonal pattern is undergoing change. It is not natural if trading day adjustment is performed because the aggregate trading day effect over a year is variable and moderately different from zero.
Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.