Psaradakis, Zacharias and Vavra, Marian (2017) Normality tests for dependent data: large-sample and bootstrap approaches. Working Paper. Birkbeck, University of London, London, UK.
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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: | Monograph (Working Paper) |
|---|---|
| Additional Information: | BWPEF 1706 |
| Keyword(s) / Subject(s): | Autoregressive sieve bootstrap, Normality test, Weak dependence |
| School: | Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School |
| Depositing User: | Administrator |
| Date Deposited: | 25 Mar 2019 13:41 |
| Last Modified: | 02 Aug 2023 17:49 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/26861 |
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