Learning in linear models with expectational leads

Models with expectational leads typically admit multiple rational expectations solutions. Based on the ordinary least-squares algorithm, this paper provides an adaptive learning scheme which allows a forecasting agent to select a particular solution on economic grounds. Conditions are given under which this scheme converges to rational expectations solutions globally for all initial conditions. We strengthen convergence results in relaxing standard assumptions and in providing conditions ensuring algorithm convergence which are easier to verify and to interpret than those previously known.     

Government policy to reduce pollution emissions within the overlapping generations model

This article develops an overlapping generations model with multiple categories of capital. The importance of this article is in its ability to analyse changes in the distribution of various categories of capital along the growth path of the economy. Economic growth is accompanied by capital growth as well as increase in pollution emissions. Implementing a government policy to reduce pollution emission would change the equilibrium path of capital distribution. Within the model, the government builds a corporate tax function that defines the tax rate as a function of a ‘desired’ pollution level. The tax rate decreases as the ‘desired’ pollution level is higher. When the ‘desired’ pollution level is higher than the actual pollution level, production is subsidized and pollution levels rise. An example and a simulation are presented in order to confirm the theoretical results and demonstrate that the model can be used for empirical analysis.     

Minimal state variable solutions to Markov-switching rational expectations models

We develop a new method for deriving minimal state variable (MSV) equilibria of a general class of Markov switching rational expectations models and a new algorithm for computing these equilibria. We compare our approach to previously known algorithms, and we demonstrate that ours is both efficient and more reliable than previous methods in the sense that it is able to find MSV equilibria that previously known algorithms cannot. Further, our algorithm can find all possible MSV equilibria in models. This feature is essential if one is interested in using a likelihood based approach to estimation.