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Data Transformation and Forecasting in Models with Unit Roots and Cointegration

Author

Listed:
  • 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.

Suggested Citation

  • John C. Chao & Valentina Corradi & Norman R. Swanson, 2001. "Data Transformation and Forecasting in Models with Unit Roots and Cointegration," Annals of Economics and Finance, Society for AEF, vol. 2(1), pages 59-76, May.
    • Handle: RePEc:cuf:journl:y:2001:v:2:i:1:p:59-76
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    Citations

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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.

    More about this item

    Keywords

    Integratedness; Cointegratedness; Nonlinear transformation;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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