Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/89431
Authors: 
Capasso, Marco
Alessi, Lucia
Barigozzi, Matteo
Fagiolo, Giorgio
Year of Publication: 
2007
Series/Report no.: 
LEM Working Paper Series 2007/23
Abstract: 
This note discusses some problems possibly arising when approximating via Monte-Carlo simulations the distributions of goodness-of-fit test statistics based on the empirical distribution function. We argue that failing to reestimate unknown parameters on each simulated Monte-Carlo sample - and thus avoiding to employ this information to build the test statistic - may lead to wrong, overly-conservative testing. Furthermore, we present a simple example suggesting that the impact of this possible mistake may turn out to be dramatic and does not vanish as the sample size increases.
Subjects: 
Goodness of fit tests
Critical values
Anderson - Darling statistic
Kolmogorov - Smirnov statistic
Kuiper statistic
Cramér - Von Mises statistic
Empirical distribution function
Monte-Carlo simulations
JEL: 
C12
C15
C63
Document Type: 
Working Paper

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