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

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