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Data Transformation and Forecasting in Models with Unit Roots and Cointegration

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Author Info

  • John C. Chao

    (University of Maryland)

  • Valentina Corradi

    (University of Exeter)

  • Norman R. Swanson

    ()
    (Department of Economics, Texas A&M University)

Abstract

We perform a series of Monte Carlo experiments in order to evaluate the impact of data transformation on forecasting models, and find that vector error-corrections dominate differenced data vector autoregressions when the correct data transformation is used, but not when data are incorrectly tansformed, even if the true model contains cointegrating restrictions. We argue that one reason for this is the failure of standard unit root and cointegration tests under incorrect data transformation.

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File URL: http://www.aeconf.net/Articles/May2001/aef020103.pdf
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Bibliographic Info

Article provided by Society for AEF in its journal Annals of Economics and Finance.

Volume (Year): 2 (2001)
Issue (Month): 1 (May)
Pages: 59-76

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Handle: RePEc:cuf:journl:y:2001:v:2:i:1:p:59-76

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Related research

Keywords: Integratedness; Cointegratedness; Nonlinear transformation;

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Cited by:
  1. Corradi, Valentina & Swanson, Norman R., 2006. "The effect of data transformation on common cycle, cointegration, and unit root tests: Monte Carlo results and a simple test," Journal of Econometrics, Elsevier, vol. 132(1), pages 195-229, May.

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