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An Historical Perspective on Forecast Errors

Published online by Cambridge University Press:  26 March 2020

Michael P. Clements  and
David F. Hendry
Michael P. Clements
Affiliation:
Department of Economics, University of Warwick
David F. Hendry
Affiliation:
Department of Economics, University of Oxford
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Abstract

Using annual observations on industrial production over the last three centuries, and on GDP over a 100-year period, we seek an historical perspective on the forecastability of these UK output measures. The series are dominated by strong upward trends, so we consider various specifications of this, including the local linear trend structural time-series model, which allows the level and slope of the trend to vary. Our results are not unduly sensitive to how the trend in the series is modelled: the average sizes of the forecast errors of all models, and the wide span of prediction intervals, attests to a great deal of uncertainty in the economic environment. It appears that, from an historical perspective, the postwar period has been relatively more forecastable.

Type
Articles
Information
National Institute Economic Review , Volume 177 , July 2001 , pp. 100 - 112
Copyright
Copyright © 2001 National Institute of Economic and Social Research

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Footnotes

∗

Financial support from the UK Economic and Social Research Council under grant no. L116251015 is gratefully acknowledged by both authors. All computations were performed using code written in Gauss. Tommaso Proietti kindly provided the Gauss code to estimate the local linear trend model.

References

Box, G.E.P. and Jenkins, G.M. (1976), Time Series Analysis, Forecasting and Control, San Francisco, Holden-Day (first published 1970).Google Scholar
Central Statistical Office (1993), Economic Trends Annual Supplement, London, HMSO.Google Scholar
Chong, Y.Y. and Hendry, D.F. (1986), ‘Econometric evaluation of linear macro-economic models’, Review of Economic Studies, 53, pp. 671–690,CrossRefGoogle Scholar
reprinted in Granger, C.W.J. (ed.) (1990), Modelling Economic Series, Oxford, Clarendon Press.Google Scholar
Christoffersen, P.F (1998), ‘Evaluating interval forecasts’, International Economic Review, 39, pp. 841–862.CrossRefGoogle Scholar
Clements, M.P. and Hendry, D.F. (1996), ‘Intercept corrections and structural change’, Journal of Applied Econometrics, 11, pp. 475–494.3.0.CO;2-9>CrossRefGoogle Scholar
Clements, M.P. and Hendry, D.F. (1998a), ‘Forecasting economic processes’, International Journal of Forecasting, 14, pp. 111–131.CrossRefGoogle Scholar
Clements, M.P. and Hendry, D.F. (1998b), Forecasting Economic Time Series: The Marshall Lectures on Economic Forecasting, Cambridge, Cambridge University Press.CrossRefGoogle Scholar
Clements, M.P. and Hendry, D.F. (1999a), Forecasting Non-stationary Economic Time Series: The Zeuthen Lectures on Economic Forecasting, Cambridge, Mass., MIT Press.Google Scholar
Clements, M.P. and Hendry, D.F. (1999b), ‘Modelling methodology and forecast failure’, Econometrics Journal (forthcoming).Google Scholar
Clements, M.P. and Hendry, D.F. (2001), ‘Forecasting with difference-stationary and trend-stationary models’, Econometrics Journal, 4, S1–S19.Google Scholar
Clements, M.P. and Taylor, N. (2001), ‘Bootstrapping prediction intervals for autoregressive models’, International Journal of Forecasting (forthcoming).CrossRefGoogle Scholar
Crafts, N.F.R. and Harley, C.K. (1992), ‘Output growth and the British Industrial Revolution: a restatement of the Crafts-Harley view’, Economic History Review, 45, pp. 703–730.CrossRefGoogle Scholar
Diebold, F.X. and Mariano, R.S. (1995), ‘Comparing predictive accuracy’, Journal of Business and Economic Statistics, 13, pp. 253–263.Google Scholar
Eitrheim, Ø, Huseb⊘, T.A. and Nymoen, R. (1999), ‘Equilibrium-correction versus differencing in macroeconometric forecasting’, Economic Modelling, 16, pp. 515–544.CrossRefGoogle Scholar
Ericsson, N.R. (1992), ‘Parameter constancy, mean square forecast errors, and measuring forecast performance: an exposition, extensions, and illustration’, Journal of Policy Modeling, 14, pp. 465–495.CrossRefGoogle Scholar
Hand, D.J. (1999), ‘Discussion contribution on “Data mining reconsidered: encompassing and the general-to-specific approach to specification search” by Hoover and Perez’, Econometrics Journal, 2, pp. 241–243.CrossRefGoogle Scholar
Harvey, A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge, Cambridge University Press.Google Scholar
Harvey, D., Leybourne, S. and Newbold, P. (1997), ‘Testing the equality of prediction mean squared errors’, International Journal of Forecasting, 13, pp. 281–291.CrossRefGoogle Scholar
Harvey, D., Leybourne, S. and Newbold, P. (1998), ‘Tests for forecast encompassing’, Journal of Business and Economic Statistics, 16, pp. 254–259.Google Scholar
Hendry, D.F. (1995), Dynamic Econometrics, Oxford, Oxford University Press.CrossRefGoogle Scholar
Hendry, D.F. (2000a), ‘Does money determine UK inflation over the long run?’ in Backhouse, R. and Salanti, A. (eds), Macroeconomics and the Real World, Oxford, Oxford University Press.Google Scholar
Hendry, D.F. (2000b), ‘On detectable and non-detectable structural change’, Structural Change and Economic Dynamics, 11, pp. 45–65.CrossRefGoogle Scholar
Kiviet, J.F. (1986), ‘On the rigor of some mis-specification tests for modelling dynamic relationships’, Review of Economic Studies, 53, pp. 241–261.CrossRefGoogle Scholar
Mellis, C. and Whittaker, R. (2000), ‘The Treasury's forecasts of GDP and the RPI: how have they changed and what are the uncertainties?’ in Holly, S. and Weale, M. (eds), Econometric Modelling: Techniques and Applications, Cambridge, Cambridge University Press.Google Scholar
Mitchell, B.R. (1988), British Historical Statistics, Cambridge, Cambridge University Press.Google Scholar
Phillips, P.C.B. (1994), ‘Bayes models and forecasts of Australian macroeconomic time series’ in Hargreaves, C. (ed.), Non-stationary Time-series Analyses and Cointegration, Oxford, Oxford University Press.Google Scholar
Poulizac, D., Weale, M. and Young, G. (1996), ‘The performance of National Institute economic forecasts’, National Institute Economic Review, 156, pp. 56–62.CrossRefGoogle Scholar
Proietti, T. (2001), ‘Forecasting with structural time series models’, mimeo, Dipartimento di Scienze Statistiche, Universita di Udine,Google Scholar
forthcoming in Clements, M.P. and Hendry, D.F. (eds) (2001), A Companion to Economic Forecasting, Oxford, Basil Blackwell.Google Scholar
Schwarz, G. (1978), ‘Estimating the dimension of a model’, Annals of Statistics, 6, pp. 461–464.CrossRefGoogle Scholar
Swanson, N.R. and White, H. (1997), ‘Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models’, International Journal of Forecasting, 13, pp. 439–462.CrossRefGoogle Scholar
Wallis, K.F. (1995), ‘Large-scale macroeconometric modelling’, in Pesaran, M.H. and Wickens, M.R. (eds), Handbook of Applied Econometrics: Macroeconomics, Oxford, Basil Blackwell.Google Scholar
West, K.D. and McCracken, M.W. (1998), ‘Regression-based tests of predictive ability’, International Economic Review, 39, pp. 817–840.CrossRefGoogle Scholar