Members Research Papers

We show how low-frequency boom and bust cycles in asset prices can emerge from Bayesian learning by investors. Investors rationally maximize infinite horizon utility but hold subjective priors about the asset return process that we allow to differ infinitesimally from the rational expectations prior.

The paper shows that US GDP velocity of M1 money has exhibited long cycles around a 1.25% per year upward trend, during the 1919-2004 period. It explains the velocity cycles through shocks constructed from a DSGE model and annual time series data (Ingram et al., 1994).

A canonical model is described which reflects the real-time informational context of decision-making. Comparisons are drawn with ‘conventional’ models that incorrectly omit market-informed insights on future macroeconomic conditions and inappropriately incorporate information that was not available at the time.

We study a stylized theory of the volatility reduction in the U.S. after 1984 - the Great Moderation - which attributes part of the stabilization to less volatile shocks and another part to more difficult inference on the part of Bayesian households attempting to learn the latent state of the economy.