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The first stage in Hendry’s reduction theory revisited

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  • SUCARRAT, Genaro

Abstract

The reduction theory of David F. Hendry provides a comprehensive probabilistic framework for the analysis and classification of the reductions associated with empirical econometric models. However, it is unable to provide an analysis on the sameunderlying probability space of the first reduction - and hence the subsequent reductions - given a commonplace theory of social reality, namely the joint hypotheses that the course of history is indeterministic, that history does not repeat itself, and that the future depends on the past. As a solution this essay proposes that the elements of the underlying outcome space in Hendry's theory are interpreted as indeterministic worlds made up of historically inherited particulars.

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  • SUCARRAT, Genaro, 2006. "The first stage in Hendry’s reduction theory revisited," LIDAM Discussion Papers CORE 2006082, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2006082
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    1. Hendry, David F. & Massmann, Michael, 2007. "Co-Breaking: Recent Advances and a Synopsis of the Literature," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 33-51, January.
    2. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Bauwens, Luc & Peter Boswijk, H. & Urbain, Jean-Pierre, 2006. "Causality and exogeneity in econometrics," Journal of Econometrics, Elsevier, vol. 132(2), pages 305-309, June.
    4. Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
    5. Geweke, John, 1984. "Inference and causality in economic time series models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 19, pages 1101-1144, Elsevier.
    6. J.‐P. Florens & M. Mouchart, 1985. "Conditioning In Dynamic Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 6(1), pages 15-34, January.
    7. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 369-380, October.
    8. Robert F. Engle & Jose Gonzalo Rangel, 2005. "The Spline GARCH Model for Unconditional Volatility and its Global Macroeconomic Causes," Working Papers 2005/13, Czech National Bank.
    9. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    10. Hendry, David F. & Richard, Jean-Francois, 1982. "On the formulation of empirical models in dynamic econometrics," Journal of Econometrics, Elsevier, vol. 20(1), pages 3-33, October.
    11. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 393-395, October.
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    More about this item

    Keywords

    theory of reduction; DGP; possible worlds; measurement error; probabilistic causality;
    All these keywords.

    JEL classification:

    • B40 - Schools of Economic Thought and Methodology - - Economic Methodology - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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