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Model Identification and Non-unique Structure

Identification is an essential attribute of any model's parameters, so we consider its three aspects of 'uniqueness', 'correspondence to reality' and 'interpretability'. Observationally-equivalent over-identified models can co-exist, and are mutually encompassing in the population; correctly-identified models need not correspond to the underlying structure; and may be wrongly interpreted. That a given model is over-identified with all over-identifying restrictions valid (even asymptotically) is insufficient to demonstrate that it is a unique representation. Moreover, structre (as invariance under extended information) need not be identifiable. We consider the role of structural breaks to discriminate between such representations.

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File URL: http://www.nuff.ox.ac.uk/economics/papers/2002/w10/DEISTGEM.pdf
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Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2002-W10.

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Length: 16 pages
Date of creation: 01 May 2001
Date of revision:
Handle: RePEc:nuf:econwp:0210
Contact details of provider: Web page: http://www.nuff.ox.ac.uk/economics/

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  1. Faust, Jon & Whiteman, Charles H., 1997. "General-to-specific procedures for fitting a data-admissible, theory-inspired, congruent, parsimonious, encompassing, weakly-exogenous, identified, structural model to the DGP: A translation and criti," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 47(1), pages 121-161, December.
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  3. Bowden, Roger J, 1973. "The Theory of Parametric Identification," Econometrica, Econometric Society, vol. 41(6), pages 1069-74, November.
  4. Hendry, David F., 2000. "On detectable and non-detectable structural change," Structural Change and Economic Dynamics, Elsevier, vol. 11(1-2), pages 45-65, July.
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  6. Søren Johansen & Katarina Juselius, 1992. "Identification of the Long-Run and the Short-Run Structure: An Application to the ISLM Model," Discussion Papers 92-04, University of Copenhagen. Department of Economics.
  7. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
  8. Hendry, David F, 1995. "Econometrics and Business Cycle Empirics," Economic Journal, Royal Economic Society, vol. 105(433), pages 1622-36, November.
  9. David F. Hendry & Katarina Juselius, 2000. "Explaining Cointegration Analysis: Part 1," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-42.
  10. Michael P. Clements & David F. Hendry, 2002. "Modelling methodology and forecast failure," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 319-344, 06.
  11. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-91, May.
  12. Deistler, Manfred, 1976. "The Identifiability of Linear Econometric Models with Autocorrelated Errors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 17(1), pages 26-46, February.
  13. Hendry, D.F. & Mizon, G.E., 1990. "Evaluating Dynamic Econometric Models By Encompassing The Var," Economics Series Working Papers 99102, University of Oxford, Department of Economics.
  14. Bontemps, Christophe & Mizon, Grayham E., 2001. "Congruence and encompassing," Discussion Paper Series In Economics And Econometrics 0107, Economics Division, School of Social Sciences, University of Southampton.
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  17. Aldrich, John, 1994. "Haavelmo's Identification Theory," Econometric Theory, Cambridge University Press, vol. 10(01), pages 198-219, March.
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  19. Hatanaka, Michio, 1975. "On the Global Identification of the Dynamic Simultaneous Equations Model with Stationary Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 16(3), pages 545-54, October.
  20. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, June.
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