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

Estimating and Testing Continuous-Time Models in Finance: The Role of Transition Densities

This article surveys recent developments to estimate and test continuous-time models in finance using discrete observations on the underlying asset price or derivative securities' prices. Both parametric and nonparametric methods are described. All these methods share a common focus on the transition density as the central object for inference and testing of the model.maximum-likelihood, diffusions, jumps, Markov processes