Academic journal article National Institute Economic Review

An Assessment of Bank of England and National Institute Inflation Forecast Uncertainties

Academic journal article National Institute Economic Review

An Assessment of Bank of England and National Institute Inflation Forecast Uncertainties

Article excerpt

This article evaluates the density forecasts of inflation published by the Bank of England and the National Institute of Economic and Social Research. It extends the analysis of the Bank of England's fan charts in an earlier article by considering data up to 2003, quarter 4, and by correcting some technical details in the light of information published on the Bank's website in Summer 2003. National Institute forecasts are also considered, although there are fewer comparable observations. Both groups' central point forecasts are found to be unbiased, but their density forecasts substantially overstated forecast uncertainty.

Introduction

In February 1996 the Bank of England and the National Institute of Economic and Social Research significantly increased the amount of information they published about the uncertainty surrounding their central projections of inflation. In effect, and in different ways, they each began to publish a density forecast of inflation, that is, an estimate of the probability distribution of possible outcomes for future inflation. The Bank represented this graphically, as a set of forecast intervals covering 10, 20, 30, ..., 90 per cent of the probability distribution, coloured red, of lighter shades for the outer bands. This was done for inflation forecasts up to eight quarters ahead, and since the distribution becomes increasingly dispersed and the intervals 'fan out' as the forecast horizon increases, the chart became known as the 'fan chart' (or, rather more informally, and noting its red colour, the 'rivers of blood'). The National Institute represented the distribution as a histogram, in the form of a table reporting the probabilities of inflation falling in various ranges. These intervals, or 'bins' of the histogram, have changed from time to time; those used currently ate: less than 1.5 per cent, 1.5 to 2.0 per cent, 2.0 to 2.5 per cent, and so on. The forecasts refer to the fourth quarters of the current and following years, and from the beginning have included not only inflation but also real GDP growth. Fan charts for real GDP growth first appeared in the Bank's Inflation Report in November 1997.

These advances in the quantification and communication of forecast uncertainty were welcome, and they have contributed to a better-informed discussion of future economic prospects. A more formal justification for the publication of density forecasts as well as point forecasts is provided by the decision theory framework. The decision theory formulation begins with a loss function L(d,y) that describes the consequences of taking decision d today if the future state variable has the value y. If the future were known, then the optimal decision would be the one that makes L as small as possible. But if the future outcome is uncertain, then the loss is a random variable, and a common criterion is to choose the decision that minimises the expected loss. To calculate the expected value of L(d,y) for a range of values of d, in order to find the minimum, the complete probability distribution of y is needed in general. The special case that justifies restricting attention to a point forecast is the case in which L is a quadratic function of y. In this case the certainty equivalence theorem states that the value of d that minimises expected loss E[L(d,y)] is the same as the value that minimises L[d,E(y)], whatever the distribution of y might be. So in this case only a point forecast, specifically the conditional expectation of the unknown future state variable, is required. In practice, however, macroeconomic forecasters have little knowledge of the identity of the users of forecasts, not to mention their loss functions, and the assumption that these are all quadratic is unrealistic. In many situations the possibility of an unlimited loss is also unrealistic, and bounded loss functions are more reasonable. These are informally referred to as 'a miss is as good as a mile' or, quoting a recent paper by Bray and Goodhart (2002), "you might as well be hung for a sheep as a lamb". …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.