2007/23 | LEM Working Paper Series | |
![]() |
||
On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters |
||
Marco Capasso, Lucia Alessi, Matteo Barigozzi, Giorgio Fagiolo |
||
Keywords | ||
![]() |
![]() |
|
Goodness of fit tests, Critical values, Anderson - Darling statistic, Kolmogorov - Smirnov statistic, Kuiper statistic, Cramer - Von Mises statistic, Empirical distribution function, Monte-Carlo simulations
|
||
JEL Classifications | ||
![]() |
![]() |
|
C12, C15, C63
|
||
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 re-estimate 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.
|
||
Downloads | ||
![]() |
![]() |
|
|
||
![]() | ||
![]() |