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Choice of Spectral Density Estimator in Ng-Perron Test: A Comparative Analysis

Author

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  • Muhammad Irfan Malik

    (International Institute of Islamic Economics, International Islamic University Islamabad, Pakistan.)

  • Atiq-ur-Rehman

    (International Institute of Islamic Economics, International Islamic University Islamabad, Pakistan.)

Abstract

Ng and Perron (2001) designed a unit root test, which incorporates the properties of DF-GLS and Phillips Perron test. Ng and Perron claim that the test performs exceptionally well especially in the presence of a negative moving average. However, the performance of the test depends heavily on the choice of the spectral density estimators used in the construction of the test. Various estimators for spectral density exist in the literature; each have a crucial impact on the output of test, however there is no clarity on which of these estimators gives the optimal size and power properties. This study aims to evaluate the performance of the Ng-Perron for different choices of spectral density estimators in the presence of a negative and positive moving average using Monte Carlo simulations. The results for large samples show that: (a) in the presence of a positive moving average, testing with the kernel based estimator gives good effective power and no size distortion, and (b) in the presence of a negative moving average, the autoregressive estimator gives better effective power, however, huge size distortion is observed in several specifications of the data-generating process.

Suggested Citation

  • Muhammad Irfan Malik & Atiq-ur-Rehman, 2015. "Choice of Spectral Density Estimator in Ng-Perron Test: A Comparative Analysis," International Econometric Review (IER), Econometric Research Association, vol. 7(2), pages 51-63, September.
  • Handle: RePEc:erh:journl:v:7:y:2015:i:2:p:51-63
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    References listed on IDEAS

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    1. Atiq-ur-Rehman, 2011. "Impact of Model Specification Decisions on Unit Root Tests," International Econometric Review (IER), Econometric Research Association, vol. 3(2), pages 22-33, September.
    2. Gilberto Libanio, 2005. "Unit roots in macroeconomic time series: theory, implications, and evidence," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 15(3), pages 145-176, September.
    3. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    4. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    5. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    6. Perron, Pierre & Ng, Serena, 1998. "An Autoregressive Spectral Density Estimator At Frequency Zero For Nonstationarity Tests," Econometric Theory, Cambridge University Press, vol. 14(5), pages 560-603, October.
    7. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    8. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    9. Dufour, Jean-Marie & King, Maxwell L., 1991. "Optimal invariant tests for the autocorrelation coefficient in linear regressions with stationary or nonstationary AR(1) errors," Journal of Econometrics, Elsevier, vol. 47(1), pages 115-143, January.
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    More about this item

    Keywords

    Ng-Perron Test; Monte Carlo; Spectral Density; Unit Root Testing.;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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