The pattern of council house sales in one housing authority, the City of Aberdeen, was monitored over a two year period since the 'Right to Buy' legislation came into force. The pattern of sales was variable, both spatially and between house types. Semi-detached and terraced properties sold at a...

We study the properties of generalized stochastic gradient (GSG) learning in forwardlooking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both di1er from and are related to E-stability, which governs stability under least squares learning. SG...

We infer determinants of Latin American hyperinflations and stabilizations by using the method of maximum likelihood to estimate a hidden Markov model that potentially assigns roles both to fundamentals in the form of government deficits that are financed by money creation and to destabilizing...

We examine optimal and other monetary policies in a linear-quadratic setup with relatively general forms of model uncertainty. The forms of uncertainty our framework encompasses include: simple i.i.d. model deviations; serially correlated model deviations; estimable regime-switching models; more...

We study the design of optimal monetary policy under uncertainty in a dynamic stochastic general equilibrium model. We use a Markov jump-linear-quadratic (MJLQ) approach to study policy design, proxying the uncertainty by different discrete modes in a Markov chain, and by taking mode-dependent...

In this paper we consider the policy implications of a fully specified dynamic general equilibrium model, developed by Smets and Wouters (2003a). This is a relatively large-scale forward looking model, which was shown to provide a good fit to the data. However there has been little previous work...

The authors use a Bayesian Markov chain Monte Carlo algorithm to estimate a model that allows temporary gaps between a true expectational Phillips curve and the monetary authority’s approximating nonexpectational Phillips curve. A dynamic programming problem implies that the monetary...

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...