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# ARMAX_FIT

Returns an array of cells for the in-sample model fitted values of the conditional mean, volatility or residuals.

## Syntax

**ARMAX_FIT**(

**Y**,

**X**,

**Order**,

**Beta**,

**mean**,

**sigma**,

**phi**,

**theta**,

**Type**)

**Y**

is the response, AKA 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^{i}).

**theta**

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

**Type**

is an integer switch to select the output type: (1=Mean (default), 2=Volatility, 3=Raw Residuals, 4=Standardized Residuals).

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

1 | Fitted mean (default) |

2 | Fitted standard deviation or volatility |

3 | Raw (non-standardized) residuals |

4 | Standardized residuals |

## Remarks

- The underlying model is described here.
- The Log-Likelihood Function (LLF
^{i}) is described here. - Each column in the explanatory factors input matrix (i.e. X) corresponds to a separate variable.
- Each row in the explanatory factors input matrix (i.e. X) corresponds to an observation.
- Observations (i.e. rows) with missing values in X or Y are assumed missing.
- The number of rows of the explanatory variable (X) must be equal to the number of rows of the response variable (Y).
- 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