Capasso, Marco Alessi, Lucia Barigozzi, Matteo Fagiolo, Giorgio
Year of Publication:
LEM Working Paper Series 2007/23
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.
Goodness of fit tests Critical values Anderson - Darling statistic Kolmogorov - Smirnov statistic Kuiper statistic Cramér - Von Mises statistic Empirical distribution function Monte-Carlo simulations