Working Papers

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.

Using a randomized control trial, we study how information treatments, differing in the precision of the signal, affect updating of inflation expectations and its implications for the process of inflation. To hypothesize the implication of receiving signals with different precision, we derive implications from a simple Bayesian updating rule. We can show that households who receive the high-variance inflation signal on average update their beliefs less often relative to households who receive the low-variance signal, implying that the mean of the posterior distribution of expectations is further away from the signal in the high-variance signal treatment than the low- variance signal treatment. In fact, in the high-variance treatment only those households that are sufficiently far from the signal update their expectations. These findings are in line with our theoretical model and provide evidence and suggest households incorporate different levels of signal uncertainty when forming inflation expectations. Consequentially, our results indicate that times of high uncertainty cause elevated inflation expectations to persist when the signal has sufficiently large variance, which has implications for the persistence of the inflation process.