NDK_SARIMAX_VALIDATE

Last Modified on 03/14/2016 1:29 pm CDT
int __stdcall NDK_SARIMAX_VALIDATE ( double  mean,
double  sigma,
WORD  nIntegral,
double *  phis,
size_t  p,
double *  thetas,
size_t  q,
WORD  nSIntegral,
WORD  nSPeriod,
double *  sPhis,
size_t  sP,
double *  sThetas,
size_t  sQ 
)

Examines the model's parameters for stability constraints (e.g. causality, invertability, stationary, etc.).

Returns
status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
Parameters
[in] mean is the model mean (i.e. mu) for the differenced series.
[in] sigma is the standard deviation of the model's residuals/innovations.
[in] nIntegral is the non-seasonal difference order
[in] phis are the coefficients's values of the non-seasonal AR component
[in] p is the order of the non-seasonal AR component
[in] thetas are the coefficients's values of the non-seasonal MA component
[in] q is the order of the non-seasonal MA component
[in] nSIntegral is the seasonal difference
[in] nSPeriod is the number of observations per one period (e.g. 12=Annual, 4=Quarter)
[in] sPhis are the coefficients's values of the seasonal AR component
[in] sP is the order of the seasonal AR component
[in] sThetas are the coefficients's values of the seasonal MA component
[in] sQ is the order of the seasonal MA component
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. NaN) at either end.
  4. SARIMAX_CHECK checks if \(\sigma\gt 0\) and if all the characteristic roots of the underlying ARMA model fall outside the unit circle.
  5. Using the Solver Add-in in Excel, you can specify the return value of SARIMAX_CHECK as a constraint to ensure a stationary ARMA model.
  6. The intercept or the regression constant term input argument is optional. If omitted, a zero value is assumed.
  7. For the input argument - Beta:
    • The input argument is optional and can be ommitted, in which case no regression component is included (i.e. plain SARIMA).
    • The order of the parameters defines how the exogneous factor input arguments are passed.
  8. The long-run mean argumen (mean) of the differenced regression residuals can take any value. If ommitted, a zero value is assumed.
  9. The residuals/innovations standard deviation (sigma) must greater than zero.
  10. For the input argument - phi (parameters of the non-seasonal AR component):
    • The input argument is optional and can be ommitted, in which case no non-seasonal AR component is included.
    • The order of the parameters starts with the lowest lag
    • 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).
  11. For the input argument - theta (parameters of the non-seasonal MA component):
    • The input argument is optional and can be ommitted, in which case no non-seasonal MA component is included.
    • The order of the parameters starts with the lowest lag
    • 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).
  12. For the input argument - sPhi (parameters of the seasonal AR component):
    • The input argument is optional and can be ommitted, in which case no seasonal AR component is included.
    • The order of the parameters starts with the lowest lag
    • 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).
  13. 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
    • 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).
  14. The non-seasonal integration order - d - is optional and can be omitted, in which case d is assumed zero.
  15. The seasonal integration order - sD - is optional and can be omitted, in which case sD is assumed zero.
  16. The season length - s - is optional and can be omitted, in which case s is assumed zero (i.e. Plain ARIMA).
Requirements
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
Examples
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
* D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906
* Box, Jenkins and Reisel; Time Series Analysis: Forecasting and Control; John Wiley & SONS.; 4th edition(Jun 30, 2008), ISBN: 470272848

See Also

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