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Can Econometrics Improve Economic Forecasting?

  • David F. Hendry
  • Michael P. Clements

After reviewing the history of analyses of economic forecasting, the role of econometrics in improving economic forecasting is considered, building on CLEMENTS and HENDRY (1994a). The basis of the analysis is a world where model selection is difficult, no model coincides with the economic mechanism, and that mechanism is both non-stationary and evolves over time. On the constructive side, econometric analysis suggests ways of reducing each of the resulting five sources of forecast uncertainty (parameter non-constancy; estimation uncertainty; variable uncertainty; innovation uncertainty; and model mis-specification). On the critical side, the lack of invariance of forecast evaluation procedures to the representation of the model may camouflage inadequate models. We show that forecasts generated from vector autoregressions in differences may be more robust to certain forms of structural change over the forecast period, and that a similar result can be achieved by suitable forms of intercept corrections in vector error-correction mechanisms.

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Article provided by Swiss Society of Economics and Statistics (SSES) in its journal Swiss Journal of Economics and Statistics.

Volume (Year): 130 (1994)
Issue (Month): III (September)
Pages: 267-298

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Handle: RePEc:ses:arsjes:1994-iii-2
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  1. Ericsson, Neil R., 1992. "Parameter constancy, mean square forecast errors, and measuring forecast performance: An exposition, extensions, and illustration," Journal of Policy Modeling, Elsevier, vol. 14(4), pages 465-495, August.
  2. Clements, M.P. & Hendry, D.F., 1992. "Forecasting in Cointegrated Systems," Economics Series Working Papers 99139, University of Oxford, Department of Economics.
  3. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  4. Fair, Ray C., 1986. "Evaluating the predictive accuracy of models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 33, pages 1979-1995 Elsevier.
  5. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
  6. Clements, M.P. & Hendry, D., 1992. "On the Limitations of Comparing Mean Square Forecast Errors," Economics Series Working Papers 99138, University of Oxford, Department of Economics.
  7. HENRY, David F. & RICHARD, Jean-François, . "On the formulation of empirical models in dynamic econometrics," CORE Discussion Papers RP -502, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. Schmidt, Peter, 1974. "The Asymptotic Distribution of Forecasts in the Dynamic Simulation of an Econometric Model," Econometrica, Econometric Society, vol. 42(2), pages 303-09, March.
  9. Howrey, E Philip & Klein, Lawrence R & McCarthy, Michael D, 1974. "Notes on Testing the Predictive Performance of Econometric Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(2), pages 366-83, June.
  10. Calzolari, Giorgio, 1981. "A Note on the Variance of Ex-Post Forecasts in Econometric Models," Econometrica, Econometric Society, vol. 49(6), pages 1593-95, November.
  11. Clements, Michael P, 1995. "Rationality and the Role of Judgement in Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 105(429), pages 410-20, March.
  12. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
  13. Schmidt, Peter, 1977. "Some Small Evidence on the Distribution of Dynamic Simulation Forecasts," Econometrica, Econometric Society, vol. 45(4), pages 997-1005, May.
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