# NDK_GARCH_GOF

 int __stdcall NDK_GARCH_GOF ( double * pData, size_t nSize, double mu, const double * Alphas, size_t p, const double * Betas, size_t q, WORD nInnovationType, double nu, WORD retType, double * retVal )

Computes the log-likelihood ((LLF), Akaike Information Criterion (AIC) or other goodness of fit function of the GARCH model.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
 [in] pData is the univariate time series data (a one dimensional array). [in] nSize is the number of observations in pData. [in] mu is the GARCH model conditional mean (i.e. mu). [in] Alphas are the parameters of the ARCH(p) component model (starting with the lowest lag). [in] p is the number of elements in Alphas array [in] Betas are the parameters of the GARCH(q) component model (starting with the lowest lag). [in] q is the number of elements in Betas array [in] nInnovationType is the probability distribution function of the innovations/residuals (see INNOVATION_TYPE)INNOVATION_GAUSSIAN Gaussian Distribution (default)INNOVATION_TDIST Student's T-Distribution,INNOVATION_GED Generalized Error Distribution (GED) [in] nu is the shape factor (or degrees of freedom) of the innovations/residuals probability distribution function. [in] retType is a switch to select a fitness measure ( see GOODNESS_OF_FIT_FUNC) [out] retVal is the calculated goodness of fit value.
Remarks
1. The underlying model is described here.
2. The Log-Likelihood Function (LLF) is described here.
3. The time series is homogeneous or equally spaced.
4. The time series may include missing values (e.g. #N/A) at either end.
5. The maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.
6. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
7. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
Requirements
 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int NDK_GARCH_GOF ( double[] pData, UIntPtr nSize, double mu, double[] Alphas, UIntPtr p, double[] Betas, UIntPtr q, short nInnovationType, double nu, short retType, ref double retVal )

Computes the log-likelihood ((LLF), Akaike Information Criterion (AIC) or other goodness of fit function of the GARCH model.

Return Value

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

 NDK_SUCCESS operation successful Error Error Code
Parameters
 [in] pData is the univariate time series data (a one dimensional array). [in] nSize is the number of observations in pData. [in] mu is the GARCH model conditional mean (i.e. mu). [in] Alphas are the parameters of the ARCH(p) component model (starting with the lowest lag). [in] p is the number of elements in Alphas array [in] Betas are the parameters of the GARCH(q) component model (starting with the lowest lag). [in] q is the number of elements in Betas array [in] nInnovationType is the probability distribution function of the innovations/residuals (see INNOVATION_TYPE)INNOVATION_GAUSSIAN Gaussian Distribution (default)INNOVATION_TDIST Student's T-Distribution,INNOVATION_GED Generalized Error Distribution (GED) [in] nu is the shape factor (or degrees of freedom) of the innovations/residuals probability distribution function. [in] retType is a switch to select a fitness measure ( see GOODNESS_OF_FIT_FUNC) [out] retVal is the calculated goodness of fit value.
Remarks
1. The underlying model is described here.
2. The Log-Likelihood Function (LLF) is described here.
3. The time series is homogeneous or equally spaced.
4. The time series may include missing values (e.g. #N/A) at either end.
5. The maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.
6. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
7. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static NumXLAPI.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