Normality Tests for Dependent Data: Large-Sample and Bootstrap Approaches

Zacharias Psaradakis and Marián Vávra
Additional contact information
Marián Vávra: National Bank of Slovakia
Authors registered in the RePEc Author Service: Marian Vavra ()

No 1706, Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics

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.

Keywords: Autoregressive sieve bootstrap; Normality test; Weak dependence. (search for similar items in EconPapers)
Date: 2017-10
New Economics Papers: this item is included in nep-ecm and nep-ets
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eprints.bbk.ac.uk/id/eprint/26861 First version, 2017 (application/pdf)

Related works:
Working Paper: Normality Tests for Dependent Data (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bbk:bbkefp:1706

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics Malet Street, London WC1E 7HX, UK.
Bibliographic data for series maintained by ( this e-mail address is bad, please contact ).