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

Muhammad Malik and Atiq Rehman

International Econometric Review (IER), 2015, vol. 7, issue 2, 51-63

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.

Keywords: Ng-Perron Test; Monte Carlo; Spectral Density; Unit Root Testing. (search for similar items in EconPapers)
JEL-codes: C01 C15 C63 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Choice of Spectral Density Estimator in Ng-Perron Test: Comparative Analysis (2014)
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