This paper introduces an event-study method that incorporates the possibility of a random event date. Consistent with empirical evidence, we assume an event may affect not only the conditional mean of a security's return, but also its conditional variance. We compare the statistical power and efficiency of our maximum-likelihood method with the standard application of traditional event-study methods to multiday security returns. Assuming a two-day event period, our empirical results provide evidence that the multiday approach is robust. We use our maximum-likelihood method to investigate the valuation effects of stock splits and stock dividends.

Relying on a simple general equilibrium model of the term structure, we show that both nominal yields and real consumption growth rates can be affine in the unobservable state variables. We can then express real consumption growth rates in terms of nominal yields rather than the unobservable state variables with the coefficients of the resultant forecasting relation being endogenously determined by the term structure model. Using term structure data over the 1985 to 2000 sample period, the empirical evidence is consistent with our model more accurately predicting real consumption growth rates than a regression model based on the term spread.

This paper investigates the behavior of the term structure of interest rates over the business cycle. In contrast to the simple change in aggregate economic activity used in previous research, we use a more appropiate measure of the business cycle: the deviation of aggregate economic activity from its potentially stochastic trend. Stochastically detrending Gross Domestic Product (GDP) by Watson's [1986] UC-ARMA methodology significantly improves the term spread's informativeness regarding future economic activity. We also investigage the implications of the UC-ARIMA representation of aggregate consumption dynamics for a linear consumption based model of the term structure. The presence of an unobserved by independent cyclical component in aggregate consumption also allows for the more efficient estimation of consumption asset pricing models.