We propose an optimal filter to transform the Conference Board Composite Leading Index (CLI) into recession probabilities in the US economy. We also analyse the CLI's accuracy...

We propose a measure of predictability based on the ratio of the expected loss of a short-run forecast to the expected loss of a long-run forecast. This predictability measure...

In the evaluation of economic forecasts, it is frequently the case that comparisons are made between a number of competing predictors. A natural question to ask in such contexts...

This paper develops non-linear smooth transition autoregressive (STAR) models with two additive smooth transition components to capture the business cycle characteristics of UK...

The use of a new bootstrap method for small-sample inference in long-horizon regressions is illustrated by analysing the long-horizon predictability of four major exchange...

In this paper we investigate the multi-period forecast performance of a number of empirical self-exciting threshold autoregressive (SETAR) models that have been proposed in the...

Monthly inflation in the United States indicates non-normality in the form of either occasional big shocks or marked changes in the level of the series. We develop a univariate...

In Bayesian analysis of vector autoregressive models, and especially in forecasting applications, the Minnesota prior of Litterman is frequently used. In many cases other prior...

In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step...

We consider the implications for forecast accuracy of imposing unit roots and cointegrating restrictions in linear systems of I(1) variables in levels, differences, and...