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Robust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation

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  • Mantalos, Panagiotis

    (Department of Business, Economics, Statistics and Informatics)

Abstract

In this paper, we introduce a set of critical values for unit root tests that are robust in the presence of conditional heteroscedasticity errors using the normalizing and variance-stabilizing transformation (NoVaS) in Politis (2007) and examine their properties using Monte Carlo methods. In terms of the size of the test, our analysis reveals that unit root tests with NoVaS-modified critical values have actual sizes close to the nominal size. For the power of the test, we find that unit root tests with NoVaS-modified critical values either have the same power as, or slightly better than, tests using conventional Dickey–Fuller critical values across the sample range considered.

Suggested Citation

  • Mantalos, Panagiotis, 2012. "Robust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation," Working Papers 2012:2, Örebro University, School of Business.
    • Handle: RePEc:hhs:oruesi:2012_002
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    References listed on IDEAS

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    1. Kim, Kiwhan & Schmidt, Peter, 1993. "Unit root tests with conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 59(3), pages 287-300, October.
    2. Davidson, Russell & MacKinnon, James G., 1994. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," Queen's Economics Department Working Papers 273307, Queen's University - Department of Economics.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
    5. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-146, March.
    6. Dimitris N. Politis, 0. "Model-free versus Model-based Volatility Prediction," Journal of Financial Econometrics, Oxford University Press, vol. 5(3), pages 358-359.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Keywords

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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