Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-22T17:43:17.353Z Has data issue: false hasContentIssue false
  • English
  • Français

The Treasury Forecasting Record: Some New Results

Published online by Cambridge University Press:  26 March 2020

Chris Melliss  and
Rod Whittaker
Get access Rights & Permissions [Opens in a new window]

Abstract

We examine the forecasting record of HM Treasury for GDP and the RPI from 1971 to the present. As well as presenting the usual statistical measures of performance, such as Root Mean Squared Errors, and regression tests of forecast efficiency and bias, we test for any relationship between the errors in GDP and RPI forecasts. Confidence intervals are constructed using a classical statistical approach based on past forecast errors, which is similar to that employed in this Review to describe forecast uncertainty.

Type
Articles
Information
National Institute Economic Review , Volume 164 , April 1998 , pp. 65 - 79
Copyright
Copyright © 1998 National Institute of Economic and Social Research

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

∗

A previous version of this paper was presented at an ESRC conference on Macroeconomic Modelling and Economic Policy, London, 8-9 January 1998. The views expressed, errors and omissions are the sole responsibility of the authors. They are grateful for comments on an earlier version of this paper from Charlie Bean, Andrew Britton, Michael Clements, Andrew Gurney, Chris Kelly, David Maude, and participants at the Conference, especially the discussant, Nigel Pain. © Crown Copyright 1998. Published by permission of Her Majesty's Stationary Office.

References

Blake, A.P. (1996), ‘Forecast error bounds by stochastic simulation’, National Institute Economic Review, no. 156, pp. 72–79.CrossRefGoogle Scholar
Britton, A. and Pain, N. (1992), ‘Economic forecasting in Britain’, National Institute Report no. 4.Google Scholar
Britton, E., Fisher, P. and Whitley, J., (1998), ‘The Inflation Report projections: understanding the fan chart’, Bank of England Quarterly Bulletin, February, pp. 30–37.Google Scholar
Clements, M.P. (1995), ‘Rationality and the role of judgement in macroeconomic forecasting’, Economic Journal, vol. 105, pp. 510–520.CrossRefGoogle Scholar
Clements, M.P. (1997), ‘Evaluating the rationality of fixed-event forecasts’, Journal of Forecasting, vol. 16, pp. 225–239.3.0.CO;2-L>CrossRefGoogle Scholar
Hendry, D.E. (1997), ‘The econometrics of macroeconomic forecasting’, Economic Journal, vol. 107, pp. 1330–1357.CrossRefGoogle Scholar
Holden, K. and Peel, D.A. (1985), ‘An evaluation of quarterly National Institute forecasts, Journal of Forecasting, vol. 4 pp. 227–234.CrossRefGoogle Scholar
Holden, K. and Peel, D.A. (1990), ‘On testing for unbiasedness and efficiency of forecasts’, The Manchester School, vol. 58, no. 2, pp. 120–127.CrossRefGoogle Scholar
H M Treasury, (1994), Memorandum on Official Economic Forecasting, House of Commons Paper 532(i), session 1990-91. Reprinted as GES Working Paper no. 121.Google Scholar
H M Treasury, (1997), The Economy and Public Finances: Supplementary Material. Pre-Budget Report Publications, November.Google Scholar
Melliss, C. (1997), ‘The Treasury forecasting record: an evaluation’, ESRC Macroeconomic Modelling Bureau Discussion Paper no. 47, August.Google Scholar
Office for National Statistics (1997), ‘Quarterly alignment adjustments in the UK National Accounts’, Economic Trends, November.Google Scholar
Pain, N. (1994), ‘Cointegration and forecast evaluation: some lessons from National Institute forecasts’, Journal of Forecasting, vol. 13, pp. 481–494.CrossRefGoogle Scholar
Poulizac, D., Weale, M. and Young, G. (1996), ‘The performance of National Institute economic forecasts’, National Institute Economic Review, no. 156, pp. 55–62.CrossRefGoogle Scholar
Theil, H. (1966), ‘Applied economic forecasting’ North-Holland Publishing, Amsterdam.Google Scholar