Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241960 
Year of Publication: 
2021
Series/Report no.: 
cemmap working paper No. CWP24/21
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
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.
Subjects: 
City size distribution
exact test
extremum test
Gibrat's law
skew normal distribution
JEL: 
C46
R11
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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