Calculates the out-of-sample simulated values.

## Syntax

**SARIMA_SIM**(

**X**,

**Order**,

**mean**,

**sigma**,

**d**,

**phi**,

**theta**,

**period**,

**sd**,

**sPhi**,

**sTheta**,

**T**,

**seed**)

**X** is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)).

**Order** is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).

Order | Description |
---|---|

1 | ascending (the first data point corresponds to the earliest date) (default) |

0 | descending (the first data point corresponds to the latest date) |

**mean** is the ARMA model mean (i.e. mu). If missing, mean is assumed zero.

**sigma** is the standard deviation value of the model's residuals/innovations.

**d** is the non-seasonal difference order.

**phi** are the parameters of the non-seasonal AR model component AR(p) (starting with the lowest lag).

**theta** are the parameters of the non-seasonal MA model component (i.e. MA(q)) (starting with the lowest lag).

**period** is the number of observations per one period (e.g. 12=Annual, 4=Quarter).

**sd** is the seasonal difference order.

**sPhi** are the parameters of the seasonal AR model component AR(p) (starting with the lowest lag).

**sTheta** are the parameters of the seasonal MA model component (i.e. MA(q)) (starting with the lowest lag).

**T** is the simulation time/horizon (expressed in terms of steps beyond the end of the time series).

**seed** is an unsigned integer for setting up the random number generator(s).

## Remarks

- The underlying model is described here.
- The Log-Likelihood Function (LLF) is described here.
- SARIMA_SIM returns an array of one simulation path starting from the end of the input data.
- The input data argument (i.e. latest observations) is optional. If omitted, an array of zeroes is assumed.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. #N/A) at either end.
- The long-run mean argument (mean) can take any value or be omitted, in which case a zero value is assumed.
- The residuals/innovations standard deviation (sigma) must be greater than zero.
- For the input argument - phi (parameters of the non-seasonal AR component):
- The input argument is optional and can be omitted, in which case no non-seasonal AR component is included.
- The order of the parameters starts with the lowest lag.
- One or more parameters may have missing values or an error code (i.e. #NUM!, #VALUE!, etc.).
- The order of the non-seasonal AR component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).

- For the input argument - theta (parameters of the non-seasonal MA component):
- The input argument is optional and can be omitted, in which case no non-seasonal MA component is included.
- The order of the parameters starts with the lowest lag.
- One or more values in the input argument can be missing or an error code (i.e. #NUM!, #VALUE!, etc.).
- The order of the non-seasonal MA component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).

- For the input argument - sPhi (parameters of the seasonal AR component):
- The input argument is optional and can be omitted, in which case no seasonal AR component is included.
- The order of the parameters starts with the lowest lag.
- One or more parameters may have missing values or an error code (i.e. #NUM!, #VALUE!, etc.).
- The order of the seasonal AR component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).

- For the input argument - sTheta (parameters of the seasonal MA component):
- The input argument is optional and can be omitted, in which case no seasonal MA component is included.
- The order of the parameters starts with the lowest lag.
- One or more values in the input argument can be missing or an error code (i.e. #NUM!, #VALUE!, etc.).
- The order of the seasonal MA component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).

- The non-seasonal integration order - d - is optional and can be omitted, in which case d is assumed to be zero.
- The seasonal integration order - sD - is optional and can be omitted, in which case sD is assumed to be zero.
- The season length - s - is optional and can be omitted, in which case s is assumed to be zero (i.e. Plain ARIMA).
- The function was added in version 1.63 SHAMROCK.

## Files Examples

## 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

## 0 Comments