We revisit predictability of forecast errors in macroeconomic survey data, which is often taken as evidence of behavioral biases at odds with rational expectations. We argue that to reject rational expectations, one must be able to predict forecast errors out of sample. However, the regressions used in the literature perform poorly out of sample in most cases. The models seem unstable and could not have helped to improve forecasts with access only to available information. We do find some notable exceptions, such as mean bias in interest rate forecasts, that survive our out-of-sample tests. Our findings thus narrow down the set of biases that merit the attention of researchers in behavioral macroeconomics.

We document novel stylized facts regarding updating of households' inflation expectations. Using two randomized controlled trials fielded in the US and Germany where signals in the form of professionals' inflation forecasts have different perceived levels of precision, we show that households react more to information with higher levels of precision, in line with Bayesian updating. However, in contrast to Bayesian updating, they mostly respond differently to these signals in the decision to update expectations (extensive margin) and not in the size of the adjustment (intensive margin). The extensive margin also displays a pronounced asymmetry: Households more frequently update their expectations when the signal is above the prior compared to when the signal is below the prior. We propose a model where households' inflation expectations exhibit state-dependent inattentiveness to inflation signals. In times of high uncertainty, elevated inflation expectations may persist due to the increased information processing costs of uncertain inflation signals and the relatively smaller welfare losses of not adjusting expectations when signals are below priors (disinflations) compared to when signals are above priors (accelerating inflation). Our model provides microfoundations for the asymmetric loss function that is commonly assumed to explain biases in inflation expectations.