Monetary models based on cash-in-advance constraints make strong predictions about the stochastic properties of endogeneous variables such as the velocity of circulation of money, the rate of inflation, and real and nominal interest rates. We develop numerical methods to understand these predictions because the models cannot be characterized analytically. We calibrate some cash-in-advance models using driving processes estimated from U. S. time-series data to generate model predictions that are compared to sample statistics. Formulations of the models that generate variability in velocity corresponding to the U.S. data typically fail along other dimensions.

[With time-varying adverse selection in the market for new equity issues, firms will prefer to issue equity when the market is most informed about the quality of the firm. This implies that equity issues tend to follow credible information releases. In addition, if the asymmetry in information increases over time between information releases, the price drop at the announcement of an equity issue should increase in the time since the last information release. Using earnings releases as a proxy for informative events, we find evidence supporting these propositions.]

With time-varying adverse selection in the market for new equity issues, firms will prefer to issue equity when the market is most informed about the quality of the firm. This implies that equity issues tend to follow credible information releases. In addition, if the asymmetry in information increases over time between information releases, the price drop at the announcement of an equity issue should increase in the time since the last information release. Using earnings releases as a proxy for informative events, we find evidence supporting these propositions.

Monetary models based on cash-in-advance constraints make strong predictions about the stochastic properties of endogeneous variables such as the velocity of circulation of money, the rate of inflation, and real and nominal interest rates. We develop numerical methods to understand these predictions because the models cannot be characterized analytically. We calibrate some cash-in-advance models using driving processes estimated from U. S. time-series data to generate model predictions that are compared to sample statistics. Formulations of the models that generate variability in velocity corresponding to the U.S. data typically fail along other dimensions.