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

Malik, Muhammad Irfan and Rehman, Atiq-ur- (2014): Choice of Spectral Density Estimator in Ng-Perron Test: Comparative Analysis.

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 negative moving average. However, the performance of test depends heavily on the choice of spectral density estimators used in the construction of test. There are various estimators for spectral density available in literature, having crucial impact on the output of test however there is no clarity on which of these estimators gives optimal size and power properties. This study aims to evaluate the performance of Ng-Perron for different choices of spectral density estimators in the presence of negative and positive moving average using Monte Carlo simulations. The results for large samples show that: (a) in the presence of positive moving average, test with kernel based estimator give good effective power and no size distortion (b) in the presence of negative moving average, autoregressive estimator gives better effective power, however, huge size distortion is observed in several specifications of data generating process

• Item Type: MPRA Paper
• Language: English
• Keywords: Ng-Perron test, Monte Carlo, Spectral Density, Unit Root Testing
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling

References

Andrews, D. W. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59(3), 817-858.

Atiq-ur-Rehman. (2011). Impact of Model Specification Decisions on Performance of Unit Root Tests. International Econometric Review, 3(2), 22-33.

Dufour, J. M., & King, M. L. (1991). Optimal invariant tests for the autocorrelation coefficient in linear regressions with stationary or nonstationary AR (1) errors. Journal of Econometrics, 47(1), 115-143.

Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient Tests for an Autoregressive Unit Root. Econometrica, 64(4), 813-836.

Libanio, G. A. (2005). Unit roots in macroeconomic time series: theory, implications, and evidence. Nova Economia Belo Horizonte, 15(3), 145-176.

Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), 1519-1554.

Phillips, P. C. (1987). Time series regression with a unit root. Econometrica, 55(2), 277-301.

Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.

Stock, J. H. (1994). Unit roots, structural breaks and trends. Handbook of Econometrics, 4, 2739-2841.