Specification tests reject a linear inflation forecasting model over the period 1959–2002. Based on this finding, we evaluate the out-of-sample inflation forecasts of a fully nonparametric model for 1994–2002. Our two main results are that: (i) nonlinear models produce much better forecasts than linear models, and (ii) including money growth in the nonparametric model yields marginal improvements, but including velocity reduces the mean squared forecast error by as much as 40%. A threshold model fits the data well over the full sample, offering an interpretation of our findings. We conclude that it is important to account for both nonlinearity and the behavior of monetary aggregates when forecasting inflation.

This paper has three aims. First, we summarise and thereby make readily available the historical time-series of quarterly density forecasts of year ahead RPIX (RPI excluding mortgage payments) inflation made by the National Institute for the period 1994Q1–2004Q4. Previous work has focused on those forecasts made in Q4 only. Secondly, we evaluate the quality of these density forecasts. Thirdly, with the benefit of hindsight we draw lessons for the future production and use of density forecasts.

This paper compares the National Institute of Economic and Social Research (NIESR) forecasts for output, inflation and key public sector finance variables against the corresponding forecasts from HM Treasury (HMT), the Bank of England (Bank) and the Institute for Fiscal Studies (IFS). We find that NIESR outperforms, on average, other major bodies in its forecasts for output and in particular inflation where simple scores are used. It also performs well on the forecasting of the government current budget surplus but not public sector net borrowing. Statistical estimates of accuracy provide a less clear picture but their reliability is blighted by the small sample size.