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

## Syntax

**GARCHM_CALIBRATE** (**[x]**, order, **[model]**, [mask], method, maxiter)

**[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). **[Model]**- Required. Is the GARCH-M model representation array (a one-dimensional array of cells (e.g., rows or columns)) (see GARCHM function).
**[Mask]**- Optional. 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**- Optional. Is the calibration/fitting method (1 = MLE, 2 = Bayesian). If missing, a Maximum Likelihood Estimate (MLE) is assumed.
Value Method 1 Maximum Likelihood Estimate (MLE) ( **default**).0 Bayesian. **MaxIter**- Optional. 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

## Related Links

## References

- James Douglas Hamilton; Time Series Analysis, Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896.
- Tsay, Ruey S.; Analysis of Financial Time Series, John Wiley & SONS; 2nd edition(Aug 30, 2005), ISBN: 0-471-690740.

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