# NDK_ACFTEST

 int __stdcall NDK_ACFTEST ( double * X, size_t N, int K, double target, double alpha, WORD method, WORD retType, double * retVal )

Calculates the p-value of the statistical test for the population autocorrelation function.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
[in] X is the univariate time series data (a one dimensional array).
[in] N is the number of observations in X.
[in] K is the lag order (e.g. k=0 (no lag), k=1 (1st lag), etc.).
[in] target is the assumed autocorrelation function value. If missing, the default of zero is assumed.
[in] alpha is the statistical significance level. If missing, a default of 5% is assumed.
[in] method is the type of test: parametric or non-parametric.
[in] retType  is a switch to select the return output:
Method Value Description
TEST_PVALUE 1 P-Value
TEST_SCORE 2 Test statistics (aka score)
TEST_CRITICALVALUE 3 Critical value.
[out] retVal  is the calculated test statistics.
Remarks
• The time series is homogeneous or equally spaced.
• The time series may include missing values (NaN) at either end.
• The lag order (k) must be less than the time series size, or an error value (#VALUE!) is returned.
• This is a two-sides (i.e. two-tails) test, so the computed p-value should be compared with half of the significance level (i.e. $$\frac{\alpha}{2}$$ ).
Requirements
Examples



 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int NDK_ACFTEST ( double[] pData, UIntPtr nSize, int nLag, double target, double alpha, UInt16 method, UInt16 retType, out double * retVal )

Calculates the p-value of the statistical test for the population autocorrelation function.

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] nLag is the lag order (e.g. k=0 (no lag), k=1 (1st lag), etc.).
[in] targetVal is the assumed autocorrelation function value. If missing, the default of zero is assumed.
[in] alpha is the statistical significance level. If missing, a default of 5% is assumed.
[in] method is the type of test: parametric or non-parametric.
[in] retType  is a switch to select the return output:
Method Value Description
TEST_PVALUE 1 P-Value
TEST_SCORE 2 Test statistics (aka score)
TEST_CRITICALVALUE 3 Critical value.
[out] retVal  is the calculated test statistics.
Remarks
• The time series is homogeneous or equally spaced.
• The time series may include missing values (NaN) at either end.
• The lag order (k) must be less than the time series size, or an error value (#VALUE!) is returned.
• This is a two-sides (i.e. two-tails) test, so the computed p-value should be compared with half of the significance level (i.e. $$\frac{\alpha}{2}$$ ).
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static NumXLAPI.DLL
Examples

References
Hull, John C.; Options, Futures and Other DerivativesFinancial Times/ Prentice Hall (2011), ISBN 978-0132777421
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