Testing economic time series for stationarity and nonstationarity

It is a well-established empirical fact that standard unit root tests fail to reject the unit root hypothesis for many economic time series. However, these results do not indicate strong evidence against relevant trend stationarity alternatives, because it is well-known that unit root tests are not very powerful.;This dissertation extends the KPSS test statistic for stationarity in two ways. First, finite sample size and power of the KPSS statistic for stationarity are extensively studied in a Monte Carlo experiment. Next the use of the KPSS statistic as a unit root test is suggested, because the KPSS statistic is consistent and a different limning distribution is obtained under the hypothesis that the series is difference stationary.;Both tests are applied to the Nelson-Plosser data, and for many of these series it is not very clear whether they contain a unit root or are trend stationary. These results are quite consistent with recent (inconclusive) empirical findings.;One implication of the above empirical findings is that many economic time series may be in the region of "near stationarity." A lot of Monte Carlo studies have shown that standard unit root tests have severe size distortions when the process is nearly stationary. This dissertation also considers the asymptotics of standard unit root tests in this case using generalized "nearly stationary model." It is found that the above size distortion problem is well predicted by our asymptotics. It is also argued that the superiority of the augmented Dickey-Fuller statistic is not established and that more efficient estimation techniques will be needed to improve the tradeoff between size distortions and low power.;Recently, various attempts, including a Bayesian approach, have been made to reconsider the important problem of distinguishing trend stationary and unit root processes. However, there have been very few previous attempts to test the null hypothesis of stationarity directly. Kwiatkowski, Phillips, Schmidt, and Shin (1992, KPSS) propose an LM test of the null hypothesis that an observable series is stationary around a deterministic trend, using the components representation in which the series is decomposed into the sum of deterministic trend, random walk, and stationary error.