We observe a rich set of public information signals available to participants in the Survey of Professional Forecasters (SPF) and decompose individual forecast revisions into those due to public information and a remainder due to residual information. We find that SPF forecasters overreact to residual information at almost all forecast horizons and for almost all forecast variables. In addition, forecasts are overly anchored to prior beliefs for all variables at all forecast horizons. We show analytically that overconfidence in private information qualitatively generates both of these features. It also implies that forecast errors correlate positively with past forecast revisions at the consensus level, but negatively at the individual level, as documented previously in the literature. Estimating Bayesian updating models on SPF data, we show that overconfidence in private information also replicates the observed patterns quantitatively. All estimated models display strong and statistically significant overconfidence in private information.

We document properties of crypto monetary policies based on a large sample of tokens. We present a dynamic model to determine the optimal issuance and fee policies for issuers. Committing to low future money growth and fees increases profits, and the degree of commitment matters for equilibrium existence. A Ramsey issuer who maximizes profits, after the initial period, makes choices that maximize the utility value of all tokens. We present a model with probabilistic commitment, solve for the steady state in closed form, and show that empirically relevant long-run money growth rates align with very high levels of commitment. • Average money growth rates decline with age and stabilize at about 0.25% per month. • We derive optimal monetary issuance and fees for a crypto issuer. • Commitment significantly influences optimal policies and achievable market capitalization. • Empirically relevant money growth rates align with very high issuer commitment.