Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-22T14:01:52.629Z Has data issue: false hasContentIssue false
  • English
  • Français

An Assessment of OECD and UK Leading Indicators

Published online by Cambridge University Press:  26 March 2020

Martin Weale
Get access Rights & Permissions [Opens in a new window]

Abstract

Leading indicators are produced by both the OECD and the UK Office of National Statistics as tools for predicting turning points of the business cycle. An assessment on the basis of performance at turning points is frustrated by their scarcity. It is found that the indicators generally have significant (but not good) ability to predict changes in the direction of the variable they are intended to lead. When they are included in VAR models the standard error of quarter on quarter changes is generally lower than when pure autoregressions are used. However, the forecasting power of such equations is poor, and the general conclusion is that such indicators are not good forecasting tools.

Type
Articles
Information
National Institute Economic Review , Volume 156 , May 1996 , pp. 63 - 71
Copyright
Copyright © 1996 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

∗

I am grateful to the Editorial Board and in particular to Nigel Pam for helpful comments.

References

Artis, M., Bladen-Hovell, R. and Zhang, W. (1995), ‘Turning points in the international business cycle: an analysis of the OECD leading indicators for the G7 countries’, OECD Economic Studies, 24, pp. 125–165.Google Scholar
Britton, A., and Pain, N. (1992), Economic Forecasting in Britain, National Institute Report no. 4.Google Scholar
Burns, A. and Mitchell, W. (1946), Measuring Business Cycles, NBER, New York.Google Scholar
Diebold, F. (1991), ‘Forecast output with the composite leading index: a real time analysis’, Journal of the American Statistical Association, 86, pp. 603–610.10.1080/01621459.1991.10475085CrossRefGoogle Scholar
Hendry, D. and Emerson, R. (1995), ‘An evaluation of forecasting using leading indicators’, Royal Economic Society Conferences, Canterbury.Google Scholar
Koopmans, T. (1947), ‘Measurement without theory’, Review of Economics and Statistics, vol. 29.Google Scholar
Nelson, A. and Plosser, A. (1982), ‘Trends and random walks in macroeconomic series’, Journal of Monetary Economics, 10, pp. 139–162.10.1016/0304-3932(82)90012-5CrossRefGoogle Scholar
Nilsson, R. (1987), ‘OECD leading indicators’, OECD Economic Studies, no. 9, pp. 105–146.Google Scholar
O'Dea, D. (1975), Cyclical Indicators for the Post-War British Economy, Cambridge University Press.Google Scholar
Osborn, D.R. (1995), ‘Moving average detrending and the analysis of business cycles’, Oxford Bulletin of Economics and Statistics, no. 57(4), pp. 547–558.10.1111/j.1468-0084.1995.tb00039.xCrossRefGoogle Scholar
Pain, N. (1994), ‘Cointegration and forecast evaluation: some lessons from National Institute forecasts’, Journal of Forecasting, no. 13, pp. 481–493.CrossRefGoogle Scholar
Slutsky, E. (1937), ‘The summation of random causes as a source of cyclical processes’, Econometrica, no. 5, pp. 105–146.CrossRefGoogle Scholar
Stock, J. and Watson, M. (1991), ‘A probability model of the co-incident indicators’, in Moore, G. and Lahiri, K., eds, Leading Economic Indicators, Cambridge University Press, pp. 63–90.Google Scholar
Yeend, C. (1996), ‘Cyclical indicators for the UK economy’, Economic Trends, 509, pp. 34–38.Google Scholar