Normal but Skewed?

Dante Amengual, Xinyue Bei () and Enrique Sentana
Additional contact information
Xinyue Bei: Duke University, https://www.duke.edu/

Working Papers from CEMFI

Abstract: We propose a multivariate normality test against skew normal distributions using higher-order log-likelihood derivatives which is asymptotically equivalent to the likelihood ratio but only requires estimation under the null. Numerically, it is the supremum of the univariate skewness coefficient test over all linear combinations of the variables. We can simulate its exact finite sample distribution for any multivariate dimension and sample size. Our Monte Carlo exercises confirm its power advantages over alternative approaches. Finally, we apply it to the joint distribution of US city sizes in two consecutive censuses finding that non-normality is very clearly seen in their growth rates.

Keywords: City size distribution; exact test; extremum test; Gibrat's law; skew normal distribution. (search for similar items in EconPapers)
JEL-codes: C46 R11 (search for similar items in EconPapers)
Date: 2021-05
New Economics Papers: this item is included in nep-ecm, nep-isf and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.cemfi.es/ftp/wp/2104.pdf (application/pdf)

Related works:
Journal Article: Normal but skewed? (2022)
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:cmf:wpaper:wp2021_2104

Access Statistics for this paper

More papers in Working Papers from CEMFI Contact information at EDIRC.
Bibliographic data for series maintained by Araceli Requerey ().