Calculates the out-of-sample conditional forecast (i.e. mean, error and confidence interval)

## Syntax

**ARMAX_FORE**(

**Y**,

**X**,

**Order**,

**Beta**,

**mean**,

**sigma**,

**phi**,

**theta**,

**T**,

**Type**,

**alpha**)

**Y** is the response or the dependent variable time series data array (one dimensional array of cells (e.g. rows or columns)).

**X** is the independent variables (exogenous factors) time series data matrix, such that each column represents one variable.

**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) |

**Beta** are the coefficients array of the exogenous factors.

**mean** is the ARMA long-run mean (i.e. mu).

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

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

**theta** are the parameters of the MA(q) component model (starting with the lowest lag).

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

**Type** is an integer switch to select the forecast output type: (1=mean (default), 2=Std. Error, 3=Term Struct, 4=LL, 5=UL)

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

1 | Mean forecast value (default) |

2 | Forecast standard error (aka local volatility) |

3 | Volatility term structure |

4 | Lower limit of the forecast confidence interval. |

5 | Upper limit of the forecast confidence interval. |

**alpha** is the statistical significance level. If missing, a default of 5% is assumed.

## Remarks

- The underlying model is described here.
- The Log-Likelihood Function (LLF) 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.
- The long-run 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 (beta):
- The input argument is optional and can be omitted, in which case no regression component is included (i.e. plain ARMA).
- The order of the parameters defines how the exogenous factor input arguments are passed.
- One or more parameters may have missing values or error codes (i.e. #NUM!, #VALUE!, etc.).

- For the input argument (phi):
- The input argument is optional and can be omitted, in which case no AR component is included.
- The order of the parameters starts with the lowest lag.
- One or more parameters may have missing values or error codes (i.e. #NUM!, #VALUE!, etc.).
- The order of the 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):
- The input argument is optional and can be omitted, in which case no 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 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 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

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