Models for decision-making under uncertainty use probability distributions to represent variables whose values are unknown when the decisions are to be made. Often the distributions are estimated with observed data. Sometimes these variables depend on the decisions but the dependence is ignored...

Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. A new class of recursive stochastic gradient algorithms designed for the k-medians loss criterion is proposed. By their recursive nature, these algorithms are very fast...

In this paper, we consider a dynamic New Keynesian model of the small open economy in the light of bounded rationality. This entails private agents and the central bank updating their beliefs about the laws of motion of inflation, the output gap and real exchange rate, when forming their optimal...

The stability and ergodicity properties of two adaptive random walk Metropolis algorithms are considered. Both algorithms adjust the scaling of the proposal distribution continuously based on the observed acceptance probability. Unlike the previously proposed forms of the algorithms, the adapted...

This paper provides an adaptive learning algorithm for linear stochastic models with expectational leads in which forecasts for an arbitrary period ahead of the current state feed back into the economic system. The concept of an unbiased forecasting rule with generates rational expectations...