Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models
Abstract
The academic system incentivizes p-hacking, where researchers select estimates and statistics with statistically significant p-values for publication. We analyze the complete process of Granger causality testing including p-hacking using Monte Carlo simulations. If the degrees of freedom of the underlying vector autoregressive model are small to moderate, information criteria tend to overfit the lag length and overfitted vector autoregressive...
Paper Details
Title
Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models
Published Date
Jun 4, 2018
Journal
Volume
56
Issue
3
Pages
797 - 830
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