Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Risk and the cross section of stock returns

This paper mathematically transforms unobservable rational expectation equilibrium model parameters (information precision and supply uncertainty) into a single variable that is correlated with expected returns and that can be estimated with recently observed data. Our variable can be used to explain the cross section of returns in theoretical, numerical, and empirical analyses. Using Center for Research in Security Prices data, we show that a -1 sigma to +1 sigma change in our variable is associated with a 0.31% difference in average returns the following month (equaling 3.78% per annum). The results are statistically significant at the 1% level. Our results remain economically and statistically significant after controlling for stocks' market capitalizations, book-to-market ratios, liquidities, and the probabilities of information-based trading

Common Factors, Information and Portfolio Choice

International audienc