In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the …-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 … teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors …

size and trend of fiscal forecasting errors, at the aggregated and disaggregated levels. The period 2013 to 2016 will be … used to test the impact of fiscal rules on the size of forecasting errors. Results show that expenditure and revenue errors …

from index ETFs with higher tracking errors. Despite tracking error being a performance predictor, investors' flow is … restricted access to index ETFs, thus not optimally chasing index ETFs with lower tracking errors. Our long-only trading strategy … that buys S&P 500 index ETFs with lowest-quintile tracking errors outperforms the equal-weighted portfolio of all S&P 500 …

We study the size and drivers of non-standard errors (Menkveld et al., 2021) in portfolio sorts across 14 common … methodological decision nodes and 40 sorting variables. These non-standard errors range between 0.05 and 0.26 percent and are, on … average, larger than standard errors. Supposedly innocuous decisions cause large variation in estimated premiums, standard …

Non-standard errors capture uncertainty due to variation in research design choices. We study the importance of … results in sizable non-standard errors and allows for p-hacking. Our study has important implications for model selection …

Voting errors occur when bad proposals pass (false positives) and good proposals fail (false negatives). We develop a … structural empirical framework to study voting errors for shareholder proposals. Our estimates inform policy debates on proxy … contrast, ISS recommendations are incorrect roughly half the time. Voting errors are costly – stock prices respond negatively …