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

## Syntax

**EGARCH_CALIBRATE** (**[x]**, order, µ, **[α]**, [γ], [β], f, ν, [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). **µ**- Optional. Is the GARCH model long-run mean (i.e., mu). If missing, the process mean is assumed to be zero.
**[α]**- Required. Are the parameters of the ARCH(p) component model: [αo α1, α2 … αp] (starting with the lowest lag).
**[γ]**- Optional. Are the leverage parameters: [γ1, γ2 … γp] (starting with the lowest lag).
**[β]**- Optional. Are the parameters of the GARCH(q) component model: [β1, β2 … βq] (starting with the lowest lag).
**F**- Optional. Is the probability distribution function of the innovations/residuals (1 = Gaussian (default), 2 = t-Distribution, 3 = GED).
Value Probability Distribution 1 Gaussian or Normal Distribution ( **default**).2 Student's t-Distribution. 3 Generalized Error Distribution (GED). **ν**- Optional. Is the shape parameter (or degrees of freedom) of the innovations/residuals’ probability distribution 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**).2 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.

## Comments

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