Data Transformation and Forecasting in Models with Unit Roots and Cointegration
AbstractWe 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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Bibliographic InfoArticle provided by Society for AEF in its journal Annals of Economics and Finance.
Volume (Year): 2 (2001)
Issue (Month): 1 (May)
Integratedness; Cointegratedness; Nonlinear transformation;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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- 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.
- Valentina Corradi & Norman R. Swanson, 2003. "The Effect of Data Transformation on Common Cycle, Cointegration and Unit Root Tests: Monte Carlo Results and a Simple Test," Departmental Working Papers 200322, Rutgers University, Department of Economics.
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