Goodness‐of‐fit Test in Parametric Time Series Models | Zendy

Prewitt Kathryn | Zendy

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Goodness‐of‐fit Test in Parametric Time Series Models

Author(s) -

Prewitt Kathryn

Publication year - 1998

Publication title -

journal of time series analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.576

H-Index - 54

eISSN - 1467-9892

pISSN - 0143-9782

DOI - 10.1111/1467-9892.00108

Subject(s) - mathematics , nonparametric statistics , test statistic , goodness of fit , kernel density estimation , estimator , parametric statistics , statistics , null hypothesis , null distribution , statistic , parametric model , goldfeld–quandt test , semiparametric regression , statistical hypothesis testing , nonparametric regression , bandwidth (computing) , z test , computer science , computer network

A goodness‐of‐fit test is proposed which uses nonparametric curve estimation methods to investigate the fit of parametric models for the spectral density. A test of the null hypothesis that the function has parametric form is considered with a test statistic which compares parametric estimates and nonparametric kernel estimates of the function and its derivatives at a preselected number of points. An important issue for the nonparametric estimator is bandwidth choice, and we propose a data‐adaptive method for local bandwidth choice. Under the null hypothesis, asymptotically the test statistic has a χ 2 distribution. Some practical issues are discussed.

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