Arbeitspapier

Normal but skewed?

We propose a multivariate normality test against skew normal distributions using higher-order loglikelihood 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.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP24/21

Klassifikation
Wirtschaft
Specific Distributions; Specific Statistics
Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
Thema
City size distribution
exact test
extremum test
Gibrat's law
skew normal distribution

Ereignis
Geistige Schöpfung
(wer)
Amengual, Dante
Bei, Xinye
Sentana, Enrique
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2021

DOI
doi:10.47004/wp.cem.2021.2421
Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Amengual, Dante
  • Bei, Xinye
  • Sentana, Enrique
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2021

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