This paper argues that the common practice of Bayesian estimation in applied macroeconomic DSGE modeling can lead to severely biased results when the imposed prior beliefs are misspecified. We demonstrate, through controlled Monte Carlo experiments on two canonical DSGE models (a Real Business...

In many set-identified models, it is difficult to obtain a tractable characterization of the identified set. Therefore, researchers often rely on nonsharp identification conditions, and empirical results are often based on an outer set of the identified set. This practice is often viewed as...

Modeling policy effects in the context of high-dimensional data requires a balanced consideration of omitted interaction bias and overfitting problems. This paper investigates the role of machine learning algorithms in stabilizing estimates and demonstrates the possible regularization bias...