MARCO CAPASSO (Tjalling C. Koopmans Research Institute, and Urban and Regional Research Centre Utrecht, Utrecht, The Netherlands) LUCIA ALESSI (European Central Bank, Frankfurt, Germany; Laboratory of Economics and Management, Sant'Anna School of Advanced Studies, Pisa, Italy) MATTEO BARIGOZZI (Max Planck Institute of Economics, Jena, Germany; Laboratory of Economics and Management, Sant'Anna School of Advanced Studies, Pisa, Italy) GIORGIO FAGIOLO () (Laboratory of Economics and Management Sant'Anna School of Advanced Studies Pisa, Italy)
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This paper 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 â and thus avoiding to employ this information to build the test statistic â may lead to wrong, overly-conservative. Furthermore, we present some simple examples suggesting that the impact of this possible mistake may turn out to be dramatic and does not vanish as the sample size increases.
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