Advertisement

What Can Statistics Contribute to the Analysis of Economic Structural Change?

  • Gordon J. Anderson
  • Grayham E. Mizon

Summary

The role of statistics in the detection and assimilation of structural change in econometric models is analyzed. Detection of structural change has been made much easier and more sophisticated by recent developments in graphical analysis and recursive estimation and testing techniques, particularly for use on microcomputers. A typology of models incorporating structural change is presented, and methods for discriminating between these models are considered. It is also argued that statistical tests for the hypothesis of structural constancy play an important role in the evaluation of econometric models. In addition, it is noted that major changes in the sample correlations between variables, rather than being a nuisance for econometric model builders, is in fact an important stimulus to model evaluation and improvement.

Keywords

Money Demand Conditional Model Model Misspecification Rival Model Recursive Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, G.J. (1987), Prediction tests in limited dependent variable models: A note. Journal of Econometrics, 34, 253–261.CrossRefGoogle Scholar
  2. Anderson, G.J. and Mizon, G.E. (1983), Parameter constancy tests: Old and new. Discussion Paper in Economics and Econometrics No. 8325. Economics Department, University of Southampton, UK.Google Scholar
  3. Andrews, D.W.K. and Fair, R.C. (1987), Inference in models with structural change. Cowles Foundation, Yale University, New Haven, CT.Google Scholar
  4. Baba, Y., Hendry, D.F., and Starr, R.M. (1987), US money demand 1960–1984. Discussion Paper, University of California, San Diego, CA.Google Scholar
  5. Barten, A.P. and Bronsard, L.S. (1970), Two-stage least squares estimation with shifts in the structural form. Econometrica, 38, 938–941.CrossRefGoogle Scholar
  6. Breusch, T.S. (1986), Hypothesis testing in unidentified models. Review of Economic Studies, 53, 635–651.CrossRefGoogle Scholar
  7. Brown, R.L., Durbin, J., and Evans, J.M. (1975), Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society, B-37, 149–192.Google Scholar
  8. Carr-Hill, R.A. and Stern, N.H. (1974), An econometric model of the supply and control of offences in England and Wales. Journal of Public Economics, 2, 289–318.CrossRefGoogle Scholar
  9. Chong, Y.Y. and Hendry, D.F. (1986), Econometric evaluation of linear macroeconomic models. Review of Economic Studies, 53, 671–690.CrossRefGoogle Scholar
  10. Chow, G.C. (1960), Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28, 591–605.CrossRefGoogle Scholar
  11. Chow, G.C. (1984), Random and changing coefficient models, Ch. 21 in: Z. Griliches and M.D. Intriligator (eds.), Handbook of Econometrics, Volume 2. Amsterdam: North-Holland.Google Scholar
  12. Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of UK inflation. Econometrica, 38, 410–421.Google Scholar
  13. Engle, R.F. and Granger, C.W.J. (1987), Cointegration and error correction: representation, estimation and testing. Econometrica, 55, 251–276.CrossRefGoogle Scholar
  14. Engle, R.F., Hendry, D.F., and Richard, J.-F. (1983), Exogeneity. Econometrica, 51, 277–304.CrossRefGoogle Scholar
  15. Gilbert, C.L. (1986), Professor Hendry’s econometric methodology. Oxford Bulletin of Economics and Statistics, 48 (3), 283–306.CrossRefGoogle Scholar
  16. Goldfeld, S.M. (1976), The case of the missing money. Brookings Papers in Economic Activity, 3, 683–730.CrossRefGoogle Scholar
  17. Goldfeld, S.M. and Quandt, R.E. (1973), The estimation of structural shifts by switching regressions. Annals of Economic and Social Measurement, 2, 475–485.Google Scholar
  18. Hendry, D.F. (1983), Econometric modelling: The consumption function in retrospect. Scottish Journal of Political Economy, 30, 193–220.CrossRefGoogle Scholar
  19. Hendry, D.F. and Mizon, G.E. (1978), Serial correlation as a convenient simplification not a nuisance: A comment on a study of the demand for money by the Bank of England. Economic Journal, 88, 549–563.CrossRefGoogle Scholar
  20. Hendry, D.F. and Mizon, G.E. (1985), Procrustean econometrics: Or stretching and squeezing data. Discussion Paper No. 68, Centre for Economic Policy Research, London, UK. (ISSN 0265–8003 ).Google Scholar
  21. Hendry, D.F. and Richard, J.-F. (1982), On the formulation of empirical models in dynamic econometrics. Journal of Econometrics, 20, 3–33.CrossRefGoogle Scholar
  22. Hendry, D.F. and Richard, J.-F. (1983), The econometric analysis of economic time series. International Statistical Review, 51, 111–163.CrossRefGoogle Scholar
  23. Judge, G.G., Griffiths, W.E., Hill, R.C., and Lee, T.C. (1980), The Theory and Practice of Econometrics, New York: John Wiley.Google Scholar
  24. Lo, A.W. and Newey, W.K. (1985), A large sample Chow test for the linear simultaneous equation. Economics Letters, 18, 351–353.CrossRefGoogle Scholar
  25. Lubrano, M., Pierse, R.G., and Richard, J.-F. (1986), Stability of a UK money demand equation: A Bayesian approach to testing exogeneity. Review of Economic Studies, 53, 603–634.CrossRefGoogle Scholar
  26. Lucas, R.E. (1976), Econometric policy evaluation: A critique, pp. 19–46 in: K. Brunner and A.H. Meltzer (eds.), The Phillips Curve and Labor Markets, New York: Carnegie-Rochester Conference on Public Policy 1.Google Scholar
  27. Mizon, G.E. (1977), Inferential procedures in nonlinear models: An application in a UK cross section study of factor substitution and returns to scale. Econometrica, 45, 1221–1242.CrossRefGoogle Scholar
  28. Mizon, G.E. (1984), The encompassing approach in econometrics, pp. 135–172 in: D.F. Hendry and K.F. Wallis (eds.), Econometrics and Quantitative Economics. Oxford: Basil Blackwell.Google Scholar
  29. Mizon, G.E. and Richard, J.-F. (1986), The encompassing principle and its application to nonnested hypotheses. Econometrica, 54, 657–678.CrossRefGoogle Scholar
  30. Pagan, A.R. (1987), Three econometric methodologies: a critical appraisal. Journal of Economic Surveys, 1 (1), 3–24.CrossRefGoogle Scholar
  31. Pagan, A.R. and Hall, A.D. (1983), Diagnostic tests as residual analysis. Econometric Reviews, 2 (2), 159–218.CrossRefGoogle Scholar
  32. Phillips, G.D.A. and McCabe, B.P. (1983), The independence of structural change tests in regression models. Economics Letters, 12, 283–287.CrossRefGoogle Scholar
  33. Poirier, D.J. (1976), The Economics of Structural Change. Amsterdam: North-Holland. Rea, J.D. (1978), Indeterminacy of the Chow test when the number of observations is insufficient. Econometrica, 46, 229.Google Scholar
  34. Richard, J-F. (1980), Models with several regimes and changes in exogeneity. The Review of Economic Studies, 47, 1–20.CrossRefGoogle Scholar
  35. Salkever, D.S. (1976), The use of dummy variables to compute predictions, prediction errors, and confidence intervals. Journal of Econometrics, 4, 393–397.CrossRefGoogle Scholar
  36. Spanos, A. (1987), On the statistical implications of modelling the error term: common factors, Granger causality and unit roots. Discussion Paper No.87/9, Birkbeck College, University of London.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • Gordon J. Anderson
  • Grayham E. Mizon

There are no affiliations available

Personalised recommendations