SARIMAX_PARAM - Values of the Model's Parameters

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

Syntax

SARIMAX_PARAM ([y], [x], order, [β], µ, σ, d, [φ], [θ], s, sd, [sφ], [sθ], 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, such that 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.

D

Required. Is the non-seasonal integration order.

[φ]

Optional. Are the parameters of the non-seasonal 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).

S

Optional. Is the number of observations per period (e.g., 12 = Annual, 4 = Quarter).

sD

Optional. Is the seasonal integration order.

[sφ]

Optional. Are the parameters of the seasonal AR(P) component model: [sφ1, sφ2 … sφpp] (starting with the lowest lag).

[sθ]

Optional. Are the parameters of the seasonal MA(Q) component model: [sθ1, sθ2 … sθqq] (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

1. The underlying model is described here.
2. The time series is homogeneous or equally spaced.
3. The time series may include missing values (e.g., #N/A) at either end.
4. Each column in the explanatory factors input matrix (i.e., X) corresponds to a separate variable.
5. Each row in the explanatory factors input matrix (i.e., X) corresponds to an observation.
6. Observations (i.e., rows) with missing values in X or Y are assumed to be missing.
7. The number of rows of the explanatory variable (X) must be equal to the number of rows of the response variable (Y).
8. SARIMAX_PARAM returns an array for the values (or the errors) of the model's parameters in the following order:
   1. $\alpha$ (if selected).
   2. $\beta_1,\beta_2,\cdots,\beta_b$.
   3. $\mu$.
   4. $\phi_1,\phi_2,...,\phi_p$.
   5. $\theta_1,\theta_2,...,\theta_q$.
   6. $\Phi_1,\phi_2,...,\phi_P$.
   7. $\Theta_1,\theta_2,...,\theta_Q$.
   8. $\sigma$.
9. The intercept or the regression constant term input argument is optional. If omitted, a zero value is assumed.
10. 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 SARIMA).
    • The order of the parameters defines how the exogenous factor input arguments are passed.
    • One or more parameters may have a missing value or an error code (i.e., #NUM!, #VALUE!, etc.).
11. The long-run mean argument (µ) can take any value or be omitted, in which case a zero value is assumed.
12. The residuals/innovations standard deviation - (σ) - must be greater than zero.
13. For the input argument - ([φ]) (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 values or error codes (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).
14. For the input argument - ([θ]) (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).
15. For the input argument - ([sφ]) (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 values or error codes (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).
16. For the input argument - ([sθ]) (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).
17. The non-seasonal integration order - (d) - is optional and can be omitted, in which case d is assumed to be zero.
18. The seasonal integration order - (sD) - is optional and can be omitted, in which case sD is assumed to be zero.
19. The season length - (s) - is optional and can be omitted, in which case s is assumed to be zero (i.e., plain ARIMA).
20. The function was added in version 1.63 SHAMROCK.

Files Examples

Related Links

• Wikipedia - Autoregressive moving average model.

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.