# NDK_ARMA_VALIDATE

 int __stdcall NDK_ARMA_VALIDATE ( double mean, double sigma, double * phis, size_t p, double * thetas, size_t q )

Examines the model's parameters for stability constraints (e.g. 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 ARMA model mean (i.e. mu). [in] sigma is the standard deviation of the model's residuals/innovations. [in] phis are the parameters of the AR(p) component model (starting with the lowest lag). [in] p is the number of elements in phis (order of AR component) [in] thetas are the parameters of the MA(q) component model (starting with the lowest lag). [in] q is the number of elements in thetas (order of MA component)
Remarks
1. The underlying model is described here.
2. NDK_ARMA_VALIDATE checks the process for stability: stationarity, invertability, and causality.
3. Using the Solver add-in in Excel, you can specify the return value of NDK_ARMA_VALIDATE as a constraint to ensure a stationary ARMA model.
4. The long-run mean can take any value or be omitted, in which case a zero value is assumed.
5. The residuals/innovations standard deviation (sigma) must greater than zero.
6. For the input argument - phi:
• 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.
• 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).
7. For the input argument - theta:
• 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.
• 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).
Requirements
Examples

 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int NDK_ARMA_VALIDATE ( double mean, double sigma, double[] phis, UIntPtr p, double[] thetas, UIntPr q )

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

Return Value

a value from NDK_RETCODE enumeration for the status of the call.

 NDK_SUCCESS operation successful Error Error Code
Parameters
 [in] mean is the ARMA model mean (i.e. mu). [in] sigma is the standard deviation of the model's residuals/innovations. [in] phis are the parameters of the AR(p) component model (starting with the lowest lag). [in] p is the number of elements in phis (order of AR component) [in] thetas are the parameters of the MA(q) component model (starting with the lowest lag). [in] q is the number of elements in thetas (order of MA component)
Remarks
1. The underlying model is described here.
2. NDK_ARMA_VALIDATE checks the process for stability: stationarity, invertability, and causality.
3. Using the Solver add-in in Excel, you can specify the return value of NDK_ARMA_VALIDATE as a constraint to ensure a stationary ARMA model.
4. The long-run mean can take any value or be omitted, in which case a zero value is assumed.
5. The residuals/innovations standard deviation (sigma) must greater than zero.
6. For the input argument - phi:
• 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.
• 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).
7. For the input argument - theta:
• 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.
• 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).
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static NumXLAPI.DLL
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