Psaradakis, Zacharias and Vávra, M. (2020) Normality tests for dependent data: large-sample and bootstrap approaches. Communications in Statistics - Simulation and Computation 49 (2), pp. 283-304. ISSN 0361-0918.
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Abstract
The paper considers the problem of testing for normality of the one-dimensional marginal distribution of a strictly stationary and weakly dependent stochastic process. The possibility of using an autoregressive sieve bootstrap procedure to obtain critical values and P-values for normality tests is explored. The small-sample properties of a variety of tests are investigated in an extensive set of Monte Carlo experiments. The bootstrap version of the classical skewness--kurtosis test is shown to have the best overall performance in small samples.
Metadata
| Item Type: | Article |
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| Additional Information: | This is an Accepted Manuscript of an article published by Taylor & Francis, available online at the link above. |
| School: | Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School |
| Depositing User: | Zacharias Psaradakis |
| Date Deposited: | 31 May 2018 15:14 |
| Last Modified: | 02 Aug 2023 17:42 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/22612 |
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