Parameters series series to be seasonally adjusted. Usually this first pass is not of interest, and by default no tables are printed. This method uses an ARIMA model estimated from the original data to extend the series one or more years. Graduating extremes reduces the effect of outliers on the estimates of the seasonal factors. This should result in better estimates of the seasonal factors and, thus, smaller revisions in Table D11 as more data become available.
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The value which gives the old adjustment is —1, that is the day after Thanksgiving. This generates a dummy which splits this between August and September based upon the number of days falling in each month. This should result in aruma estimates of the seasonal factors and, thus, smaller revisions in Table D11 as more data become available. Arrima series is adjusted using the log-additive model, with a full set of printed output.
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The X method uses a set of centered moving averages to estimate the seasonal components. PRINTALL provides the same output as the default printing for all models fit and, in addition, prints an estimation summary and chi-square statistics for each model fit.
PROC X ARIMA Statement -
Please contact Estima if you are interested in upgrading to the Professional version. If, instead, the series shows a fairly sharp break in its seasonal pattern, or if the seasonal is more a function of some other variable e. By default, range of series.
In some cases, however, they can be preferable. A general power transformation of the form is obtained by specifying. In these cases, an initial pass of the standard X11 method is required to get rid of calendar effects before doing any ARIMA estimation.
Parameters series series to be seasonally adjusted. Note that centering is done after differencing. The specified transformation is applied only to a user-specified model.
Criteria Summary for Model 2: The MODE option chooses which of the four adjustment modes to use.
This method was tested against a large number of Canadian economic series and was found to greatly reduce the amount of revisions arimq new data were added. See the section Details of Model Selection for details.
X-12-ARIMA
Nonrejection of the hypothesis is evidence for an adequate model. NOINT suppresses the fitting of a constant or intercept parameter in the model. Later, when forecasts are generated, the mean is added back. The hypothesis being tested is that of model adequacy.
This reports on every step in the main adjustment process. By default, this xx11 upon the data. This has no effect on the level of precision of the calculations or the output series, just the printed output.
X11 Instruction
This is the standard level of X11 output—still primarily the final estimates. Arrima can only be applied to monthly or quarterly series containing at least three years of data. An inherent problem with the X method is the revision of the seasonal factor estimates as new data become available. The internal regression used for the four options above includes automatic detection and removal of additive outliers.
The X11 Procedure
These are added sequentially using a forward pass and possibly deleted using a backwards one. The following partial listing shows parameter estimates and model diagnostics for the ARIMA model chosen by the automatic selection process.
The arlma is done on the centered data. Data points for which an additive dummy has a t -statistic bigger than the indicated limit are removed from the regression.
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