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

## Syntax

**SARIMAX_PARAM**(

**Y**,

**X**,

**Order**,

**Beta**,

**mean**,

**sigma**,

**d**,

**phi**,

**theta**,

**period**,

**sd**,

**sPhi**,

**sTheta**,

**Type**,

**maxIter**)

- Y
- is the response or 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 SARIMA model mean (i.e. long-run of the differenced time series). If missing, mean is assumed zero.
- sigma
- is the standard deviation value of the model's residuals/innovations.
- d
- is the non-seasonal difference order.
- phi
- are the parameters of the non-seasonal AR model component AR(p) (starting with the lowest lag).
- theta
- are the parameters of the non-seasonal MA model component (i.e. MA(q)) (starting with the lowest lag).
- period
- is the the number of observations per one period (e.g. 12=Annual, 4=Quarter).
- sd
- is the seasonal difference order.
- sPhi
- are the parameters of the seasonal AR model component AR(p) (starting with the lowest lag).
- sTheta
- are the parameters of the seasonal MA model component (i.e. MA(q)) (starting with the lowest lag).
- Type
- is an integer switch to select the output array: (1=Quick Guess (default), 2= Calibrated , 3=Std. Errors)
Order Description 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
- 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 missing.
- The number of rows of the explanatory variable (X) must be at 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.
- SARIMAX_PARAM returns an array for the values (or the errors) of the model's parameters in the following order:

- $\alpha$: (if selected)
- $\beta_1,\beta_2,\cdots,\beta_b$
- $\mu$
- $\phi_1,\phi_2,...,\phi_p$
- $\theta_1,\theta_2,...,\theta_q$
- $\Phi_1,\phi_2,...,\phi_P$
- $\Theta_1,\theta_2,...,\theta_Q$
- $\sigma$

- The intercept or the regression constant term input argument is optional. If omitted, a zero value is assumed.
- 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 SARIMA).
- The order of the parameters defines how the exogenous factor input arguments are passed.
- One or more parameters may have missing value or an error code(i.e. #NUM!, #VALUE!, etc.).

- The long-run mean argument (mean) of the differenced regression residuals can take any value. If omitted, a zero value is assumed.
- The residuals/innovations standard deviation (sigma) must greater than zero.
- For the input argument - phi (parameters of the non-seasonal AR component):
- The input argument is optional and can be omitted, in which case no non-seasonal AR component is included.
- The order of the parameters starts with the lowest lag
- One or more parameters may have missing value or an error code(i.e. #NUM!, #VALUE!, etc.).
- The order of the non-seasonal 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 (parameters of the non-seasonal MA component):
- The input argument is optional and can be omitted, in which case no non-seasonal 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 non-seasonal MA 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 - sPhi (parameters of the seasonal AR component):
- The input argument is optional and can be omitted, in which case no seasonal AR component is included.
- The order of the parameters starts with the lowest lag
- One or more parameters may have missing value or an error code(i.e. #NUM!, #VALUE!, etc.).
- The order of the seasonal 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 - sTheta (parameters of the seasonal MA component):
- The input argument is optional and can be omitted, in which case no seasonal 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 seasonal 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 non-seasonal integration order - d - is optional and can be omitted, in which case d is assumed zero.
- The seasonal integration order - sD - is optional and can be omitted, in which case sD is assumed zero.
- The season length - s - is optional and can be omitted, in which case s is assumed zero (i.e. Plain ARIMA).
- The function was added in version 1.63 SHAMROCK.

## Files Examples

## Related Links

## 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

## Comments

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