You can manage bookmarks using lists, please log in to your user account for this.
Media type:
Report;
E-Book
Title:
On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters
Contributor:
Capasso, Marco
[Author];
Alessi, Lucia
[Author];
Barigozzi, Matteo
[Author];
Fagiolo, Giorgio
[Author]
imprint:
Pisa: Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), 2007
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
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.