A New Model Of Trend Inflation

This paper introduces a new model of trend (or underlying) inflation. In contrast to many earlier approaches, which allow for trend inflation to evolve according to a random walk, ours is a bounded model which ensures that trend inflation is constrained to lie in an interval. The bounds of this interval can either be fixed or estimated from the data. Our model also allows for a time-varying degree of persistence in the transitory component of inflation. The bounds placed on trend inflation mean that standard econometric methods for estimating linear Gaussian state space models cannot be used and we develop a posterior simulation algorithm for estimating the bounded trend inflation model. In an empirical exercise with CPI inflation we find the model to work well, yielding more sensible measures of trend inflation and forecasting better than popular alternatives such as the unobserved components stochastic volatility model.

Caught Between Theory and Practice: Government, Market, and Regulatory Failure in Electricity Sector Reforms

The world-wide electricity sector reforms of the early 1990s have revealed the considerable complexities of making market driven reforms in network and infrastructure industries. This paper reflects on the experiences to date with the process and outcomes of marketbased electricity reforms across less-developed, transition and developed economies. The reforms outcomes suggest similar problems facing the electricity sector of these countries though their contexts vary significantly. Many developing and developed economies continue to have investment inadequacy concerns and the need to balance economy efficiency, sustainability and social equity after more than two decades of experience with reforms. We also use a case study of selected countries that in many respects represent the current state of the reform though they are rarely examined. Nepal, Belarus and Ireland are chosen as country-specific case studies for this purpose. We conclude that the changing dynamics of the electricity supply industry (ESI) and policy objectives imply that analysing the success and failure of reforms will indeed remain a complex process.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.