Returns an array of cells for the quick guess, optimal (calibrated), or standard errors of the values of the model's parameters.

## Syntax

**ARMAX_PARAM** (**[y]**, **[x]**, order, [β], µ, **σ**, [φ], [θ], return, maxiter)

**[Y]**- Required. Is the response or the dependent variable time series data array (a one-dimensional array of cells (e.g., rows or columns)).
**[X]**- Required. Is the independent variables (exogenous factors) time series data matrix, so each column represents one variable.
**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). **[β]**- Optional. Is the coefficients array of the exogenous factors.
**µ**- Optional. Is the ARMA model long-run mean (i.e., mu). If missing, the process mean is assumed to be zero.
**σ**- Required. Is the standard deviation value of the model's residuals/innovations.
**[φ]**- Optional. Are the parameters of the AR(p) component model: [φ1, φ2 … φp] (starting with the lowest lag)
**[θ]**- Optional. Are the parameters of the MA(q) component model: [θ1, θ2 … θq] (starting with the lowest lag).
**Return**- Optional. Is an integer switch to select the output array: (1 = Quick Guess (default), 2 = Calibrated, 3 = Std. Errors).
Value Return 1 Quick guess (non-optimal) of parameters' values ( **default**).2 Calibrated (optimal) values for the model's parameters. 3 Standard error of the parameters' values. **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.
- 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 to be missing.
- The number of rows of the explanatory variable (X) must equal 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.
- ARMAX_PARAM returns an array for the values (or errors) of the model's parameters values in the following order:
- $\mu$.
- $\beta_1,\beta_2,...,\beta_b$.
- $\phi_1,\phi_2,...,\phi_p$.
- $\theta_1,\theta_2,...,\theta_q$.
- $\sigma$.
- The long-run mean can take any value or be omitted, in which case a zero value is assumed.
- The residuals/innovations standard deviation (σ) must be greater than zero.
- For the input argument - ([β]):
- 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 - ([φ]):
- 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 - ([θ]):
- 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

## 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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