Elections, Fiscal Policy and Growth: Revisiting the Mechanism

This short paper reconsiders the popular result that the lower the probability of getting reelected, the stronger the incumbent politicians’ incentive to follow short-sighted, inefficient policies. The set-up is a general equilibrium model of endogenous growth and optimal fiscal policy, in which two political parties can alternate in power. We show that re-election uncertainty is not enough to produce the popular result. Specifically, re-election uncertainty must be combined with the hypothesis that politicians care about economic outcomes more when in power than when out of power, and - more importantly - that this preference over being in power is ad hoc. That is, if politicians can also choose how much to care about economic outcomes when in and out of power, it is optimal to care the same and hence shortsighted policies do not arise. Therefore, such policies presuppose a degree of irrationality on the part of political parties.politics, fiscal policy, economic growth, general equilibrium

Forecasting using Bayesian and Information Theoretic Model Averaging: An Application to UK Inflation

In recent years there has been increasing interest in forecasting methods that utilise large datasets, driven partly by the recognition that policymaking institutions need to process large quantities of information. Factor analysis is one popular way of doing this. Forecast combination is another, and it is on this that we concentrate. Bayesian model averaging methods have been widely advocated in this area, but a neglected frequentist approach is to use information theoretic based weights. We consider the use of model averaging in forecasting UK inflation with a large dataset from this perspective. We find that an information theoretic model averaging scheme can be a powerful alternative both to the more widely used Bayesian model averaging scheme and to factor models.Forecasting, Inflation, Bayesian model averaging, Akaike criteria, Forecast combining

Towards a framework for investigating tangible environments for learning

External representations have been shown to play a key role in mediating cognition. Tangible environments offer the opportunity for novel representational formats and combinations, potentially increasing representational power for supporting learning. However, we currently know little about the specific learning benefits of tangible environments, and have no established framework within which to analyse the ways that external representations work in tangible environments to support learning. Taking external representation as the central focus, this paper proposes a framework for investigating the effect of tangible technologies on interaction and cognition. Key artefact-action-representation relationships are identified, and classified to form a structure for investigating the differential cognitive effects of these features. An example scenario from our current research is presented to illustrate how the framework can be used as a method for investigating the effectiveness of differential designs for supporting science learning

Forward checking in the primal and dual constraint graphs.

Constraint Satisfaction Problems (CSPs) have been a subject of research in Artificial Intelligence for many years. CSPs are a general way of describing problems that can be used to represent many different types of real-world problems, including scheduling, planning, timetabling, and other combinatorial problems. The primal and dual constraint graphs are two ways of representing a CSP. Some CSPs have features that can be exploited by algorithms trying to find solutions. In this work, results from solving CSPs using forward-checking algorithms that use the primal- and dual-graph representations will be presented, and regions where one representation performs better than the other will be identified. 1t will be shown that the dual representation performs better than the primal representation on CSPs with tight constraints.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .P75. Source: Masters Abstracts International, Volume: 44-03, page: 1411. Thesis (M.Sc.)--University of Windsor (Canada), 2005