tests whether the data is autocorrelated.
tests whether the data is autocorrelated up to lag k.
returns the value of for a given model.
- AutocorrelationTest performs a hypothesis test for randomness on data with the null hypothesis that the autocorrelations and alternative that at least one of the .
- Rejecting the null hypothesis allows the conclusion that the data is not random.
- By default, a probability value or -value is returned.
- A small -value suggests that randomness is unlikely.
- The data can be a list of values or a TemporalData object.
- The lag k can be Automatic or a positive integer such that .
- If k is not given, it is taken to be Automatic.
- Setting k to Automatic gives k=Ceiling[Log[n]]].
- AutocorrelationTest[data,k,"test"] reports the -value according to .
- The following tests can be used for univariate data with all paths of equal length n:
- For multivariate data:
- AutocorrelationTest[data,k,"HypothesisTestData"] returns a HypothesisTestData object htd that can be used to extract additional test results and properties using the form htd["property"].
- AutocorrelationTest[data,k,"property"] can be used to directly give the value of .
- Properties related to the reporting of test results include:
"AllTests" list of all applicable tests "AutomaticTest" test chosen if Automatic is used "PValue" list of -values "PValueTable" formatted table of -values "ShortTestConclusion" a short description of the conclusion of a test "TestConclusion" a description of the conclusion of a test "TestData" list of pairs of test statistics and -values "TestDataTable" formatted table of -values and test statistics "TestStatistic" list of test statistics "TestStatisticTable" formatted table of test statistics
- The following option can be used:
SignificanceLevel 0.05 cutoff for diagnostics and reporting
- For unit root tests, a cutoff is chosen such that is rejected only if . The value of used for the and properties is controlled by the SignificanceLevel option. By default, is set to 0.05.
Introduced in 2014