We estimate a Bayesian vector autoregression for the U.K. with drifting coefficients and stochastic volatilities. We use it to characterize posterior densities for several objects that are useful for designing and evaluating monetary policy, including local approximations to the mean,...

We study an economy in which two types of agents have diverse beliefs about the law of motion for an exogenous endowment. One type knows the true law of motion, and the other learns about it via Bayes's theorem. Financial markets are incomplete, the only traded asset being a risk-free bond....

We propose a new welfare criterion that allows us to rank different financial market structures in the presence of belief heterogeneity. We analyze economies with complete and incomplete financial markets and/or restricted trading possibilities in the form of borrowing limits or transaction...

This paper uses a nonlinear stochastic model to describe inflation-unemployment dynamics in the U.S. after World War II. The model is a vector autoregression with coefficients that are random walks with innovations that are arbitrarily correlated with each other and with innovations to the...

We use Bayesian methods to estimate two models of post WWII U.S. inflation rates with drifting stochastic volatility and drifting coefficients. One model is univariate, the other a multivariate autoregression. We define the inflation gap as the deviation of inflation from a pure random walk...

For a VAR with drifting coefficients and stochastic volatilities, we present posterior densities for several objects that are of interest for designing and evaluating monetary policy. These include measures of inflation persistence, the natural rate of unemployment, a core rate of inflation, and...

Previous studies have interpreted the rise and fall of U.S. inflation after World War II in terms of the Fed's changing views about the natural rate hypothesis but have left an important question unanswered. Why was the Fed so slow to implement the low-inflation policy recommended by a natural...

We study a Markov decision problem with unknown transition probabilities. We compute the exact Bayesian decision rule and compare it with two approximations. The first is an infinite-history, rational-expectations approximation that assumes that the decision maker knows the transition...