Returns a unique string to designate the specified X-13ARIMA-SEATS model.

## Syntax

**X13AS** (**[x]**, order, **start**, **[priors]**, **x13spec**)

**[X]**- Required. Is the univariate time series data (a one-dimensional array of cells (e.g., rows or columns)).
**Order**- Optional. Is the time order in the data series (i.e., the first data point's corresponding date (earliest date = 1 (default), latest date = 0)).
Value Order 1 Ascending (the first data point corresponds to the earliest date) ( **default**).0 Descending (the first data point corresponds to the latest date). **Start**- Required. Is a serial number that represents the data start date.
**[Priors]**- Required. Are user-defined prior adjustment factors (a one or two-dimensional array of cells).
**X13Spec**- Required. Is a JSON-encoded string for X-13ARIMA-SEATS model specifications.

* * Status

The X13AS(.) function is available starting with version 1.67 MARTHA.

## Remarks

- The underlying X-13ARIMA-SEATS model is described here.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g., #N/A) at either end.
- If the input time series contains one or more intermediate observations with missing values, the X13Spec must specify a method for handling values.
- The input time series can be of any size, but due to the size limitation in the underlying US Census X13AS program, NumXL will use up to the most recent 780 observations and advance the start date, accordingly.
- Due to string length limitations in the Excel formula, we recommend using a reference to a cell in your workbook, whose value contains the actual X13Spec string.
- X13AS(.) generates the specification file (SPC) and all data files and runs the underlying US census x13as program, only when it detects a change in the model or data files.
- X13AS(.) calculates the unique model identifier based on the absolute cell address from which the function was called.

## Files Examples

## Related Links

## References

- Hamilton, J.D.; Time Series Analysis, Princeton University Press (1994), ISBN 0-691-04289-6.
- Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740.

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