Returns a unique string identifier to designate the specified SARIMAX model.
Syntax
SARIMAX(Beta, mean, sigma, d, phi, theta, period, sd, sPhi, sTheta)
- Beta
- are the coefficients array of the exogenous factors.
- mean
- is the ARMA model mean (i.e. long-run of the differenced regression residuals 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).
Remarks
- The underlying model is described here.
- 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
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