期刊
SCANDINAVIAN JOURNAL OF STATISTICS
卷 27, 期 1, 页码 143-176出版社
WILEY-BLACKWELL
DOI: 10.1111/1467-9469.00184
关键词
bootstrap; kernel estimators; periodogram; Whittle estimators
A new goodness-of-fit test for time series models is proposed. The test statistic is based on the distance between a kernel estimator of the ratio between the true and the hypothesized spectral density and the expected value of the estimator under the null. It provides a quantification of hom well a parametric spectral density model fits the sample spectral density (periodogram). The asymptotic distribution of the statistic proposed is derived and its power properties are discussed. To improve upon the large sample (Gaussian) approximation of the distribution of the test statistic under the null, a bootstrap procedure is presented and justified theoretically. The finite sample performance of the test is investigated through a simulation experiment and applications to real data sets are given.
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