Finite Sample Distributions and Non-normality in Second Generation Panel Unit Root Tests by

As a remarkable advantage, panel unit root testing statistics present Gaussian distribu-tion in the limit rather than the complicated functionals of Wiener processes compared with traditional single time series tests. Therefore, the asymptotic critical values are di-rectly used and the finite sample performance is not given proper attention. In addition, the unit root test literature heavily relies on the normality assumption, when this condi-tion fails, the asymptotic results are no longer valid. This thesis analyzes and finds serious finite sample bias in panel unit root tests and the systematic impact of non-normality on the tests. Using Monte Carlo simulations, in par-ticular, the application of response surface analysis with newly designed functional forms of response surface regressions, the thesis demonstrates the trend patterns of fi-nite sample bias and test bias vary closely in relation to the variation in sample size and the degree of non-normality, respectively. Finite sample critical values are then pro-posed, more importantly, the finite sample critical values are augmented by the Da-vid-Johnson estimate of percentile standard deviation to account for the randomness in