Under the assumption that individuals know the conditional distributions of signals given the payoff-relevant parameters, existing results conclude that as individuals observe infinitely many signals, their beliefs about the parameters will eventually merge. We first show that these results are...

We develop and estimate a general equilibrium model to quantitatively assess the effects and welfare implications of central bank transparency. Monetary policy can deviate from active inflation stabilization and agents conduct Bayesian learning about the nature of these deviations. Under...

Under the assumption that individuals know the conditional distributions of signals given the payoff-relevant parameters, existing results conclude that as individuals observe infinitely many signals, their beliefs about the parameters will eventually merge. We first show that these results are...

The problem of how to rationally aggregate probability measures occurs in particular (i) when a group of agents, each holding probabilistic beliefs, needs to rationalise a collective decision on the basis of a single 'aggregate belief system' and (ii) when an individual whose belief system is...

Quadratic Voting (QV) and Ramsian Voting (RV) are two methods of “Bayesian Voting” or “Bayesian preference-aggregation” that allow voters not only to rank their preferences but also to express their degree. This paper will explore the link between voter preferences and subjective...

The return and various attributes from the McGreggor database were used to estimate the return that will be achieved by different companies in the Johannesburg Stock Exchange (JSE). None of the attributes were significantly correlated to the return. The Pearl algorithm in Bayesian belief...

In this paper we evaluate the empirical relevance of learning by private agents in an estimated medium–scale DSGE model. We replace the standard rational expectation assumption in the Smets and Wouters (2007) model by a constant gain learning mechanism. If agents know the correct structure of...

Aiming at financial applications, we study the problem of learning the volatility under market microstructure noise. Specifically, we consider noisy discrete time observations from a stochastic differential equation and develop a novel computational method to learn the diffusion coefficient of...