Beating the random walk: a performance assessment of long-term interest rate forecasts

This article assesses the performance of a number of long-term interest rate forecast approaches, namely time series models, structural economic models, expert forecasts and combinations thereof. The predictive performance of these approaches is compared using outside sample forecast errors, where a random walk forecast acts as benchmark. It is found that for five major Organization for Economic Co-operation and Development (OECD) countries, namely the US, Germany, UK, The Netherlands and Japan, the other forecasting approaches do not outperform the random walk on a 3-month forecast horizon. On a 12-month forecast horizon, the random walk model is outperformed by a model that combines economic data and expert forecasts. Several methods of combination are considered: equal weights, optimized weights and weights based on the forecast error. It seems that the additional information contents of the structural models and expert knowledge adds considerably to the performance of forecasting 12 months ahead. © 2013 Taylor & Francis.