Normality Tests for Dependent Data

Zacharias Psaradakis and Marian Vavra ()

No WP 12/2017, Working and Discussion Papers from Research Department, National Bank of Slovakia

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)
JEL-codes: C12 C15 C32 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2017-12
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Normality Tests for Dependent Data: Large-Sample and Bootstrap Approaches (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:svk:wpaper:1053

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