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Covariate Unit Root Tests with Good Size and Power

Author

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  • Sebastian Fossati

    (University of Alberta, Department of Economics)

Abstract

The selection of the truncation lag for covariate unit root tests is analyzed using Monte Carlo simulation. It is shown that standard information criteria such as the BIC or the AIC can result in tests with large size distortions. Modifi ed information criteria can be used to construct tests with good size and power. An empirical illustration is provided.

Suggested Citation

  • Sebastian Fossati, 2011. "Covariate Unit Root Tests with Good Size and Power," Working Papers 2011-04, University of Alberta, Department of Economics.
    • Handle: RePEc:ris:albaec:2011_004
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    File URL: https://sites.ualberta.ca/~econwps/2011/wp2011-04.pdf
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    Cited by:

    1. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    2. Pitarakis, Jean-Yves, 2014. "A joint test for structural stability and a unit root in autoregressions," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 577-587.
    3. Grassi, S. & Proietti, T., 2014. "Characterising economic trends by Bayesian stochastic model specification search," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 359-374.
    4. Ricardo Quineche & Gabriel Rodríguez, 2017. "Selecting the Lag Length for the M GLS Unit Root Tests with Structural Change: A Warning Note for Practitioners Based on Simulations," Econometrics, MDPI, vol. 5(2), pages 1-10, April.
    5. Sebastian Fossati, 2013. "Unit root testing with stationary covariates and a structural break in the trend function," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 368-384, May.
    6. Kazuki Hiraga, 2011. "New Methods for Testing the Sustainability of Government Debt," Keio/Kyoto Joint Global COE Discussion Paper Series 2011-020, Keio/Kyoto Joint Global COE Program.

    More about this item

    Keywords

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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