computes the maximum likelihood estimate (MLE) of the model's parameters.

## Syntax

**GARCHM_CALIBRATE**(

**X**,

**Order**,

**Model**,

**Mask**,

**Method**,

**maxIter**)

**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) |

**Model** is the GARCH-M model representation array (a one dimensional array of cells (e.g. rows or columns)) (see GARCHM function).

**Mask** is an array of 0's and 1's to specify which parameters to calibrate for. If missing, all parameters are included in the calibration.

**Method** is the calibration/fitting method (1=MLE, 2=Bayesian). If missing, a Maximum Likelihood Estimate (MLE) is assumed.

Method | Description |
---|---|

1 | Maximum Likelihood Estimate (MLE) |

2 | Bayesian |

**maxIter** is the maximum number of iterations used to calibrate the model. If missing, the default maximum of 100 is assumed.

## Remarks

- The underlying 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.
- The maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.

## 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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