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.
- Language
-
Englisch
- Bibliographic citation
-
Series: cemmap working paper ; No. CWP24/21
- Classification
-
Wirtschaft
Specific Distributions; Specific Statistics
Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
- Subject
-
City size distribution
exact test
extremum test
Gibrat's law
skew normal distribution
- Event
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Geistige Schöpfung
- (who)
-
Amengual, Dante
Bei, Xinye
Sentana, Enrique
- Event
-
Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
-
2021
- DOI
-
doi:10.47004/wp.cem.2021.2421
- Handle
- Last update
-
10.03.2025, 11:45 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Amengual, Dante
- Bei, Xinye
- Sentana, Enrique
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2021
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