A model for U.S. macroeconomic time series that has been used for forecasting for several years is described in some detail. The model is a multivariate Bayesian autoregression, with allowance for conditional heteroskedasticity, stochastic time-variation in parameters, and non-normality of...

We examine the theory and behavior in practice of Bayesian and bootstrap methods for generating error bands on impulse responses in dynamic linear models. The Bayesian intervals have a firmer theoretical foundation in small samples, are easier to compute, and are about as good in small samples...

Existing theory and evidence on the effects of monetary policy are reviewed. Substantial room for disagreement among economists remains. New evidence, based on multivariate time series studies of several countries, is presented. While certain patterns in the data consistent with effective...

The martingale-equivalence condition delivered by a non-arbitrage assumption in complete asset markets has implications for fine-time-unit asset price behavior that can be rejected with finite spans of data. A class of stochastic processes that could model such deviations from...

In his paper "To Criticize the Critics" (1991), Peter Phillips discusses Bayesian methodology for time series models. The main point that Uhlig and I set out to make, however, was that careful consideration of the implications of the likelihood principle suggests that much of the recent work...

Short-term interest rates in the United States have been "too high" since 1979-10 in the sense that both unconditional and conditional forecasts, based on an estimated vector autoregression model summarizing the prior experience, underpredict short-term interest rates during this period....