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Description:
This thesis contains four essays on persistence change tests and non-stationarity tests. Persistence change tests are analysed under non-standard conditions and a new family of tests to detect changes in persistence and unit roots is proposed that is based on the CUSUM testing principle. These can be applied in economic and financial time series. Chapter 1 introduces the existence and implications of persistence in time series and structural changes. Furthermore, the impact of asymmetric volatility and different types of outliers is discussed. A new testing principle based on the concept of squared CUSUM of residuals is developed. Chapter 2 reviews the literature on different methods for persistence change tests including parametric and non-parametric modifications. A family GARCH model is presented to consider different asymmetric conditional volatility models within the persistence change model. The Wild bootstrap approach is introduced and bootstrap analogues of the persistence change tests are derived. The bootstrap procedure is conducted in a comprehensive Monte Carlo study to analyse the behaviour of the tests under asymmetric volatility. The results show that the tests suffer from severe size distortions, while the bootstrap method provides reasonable results in small samples. In an application to the U.S. stock market, asymmetric volatility models are estimated on the return series, where the persistence change tests and the bootstrap analogues are conducted. The main finding is that the tests falsely detect a change in persistence under asymmetric volatility, while the bootstrap analogues assume stationary behaviour. In chapter 3 the effect of outliers on inference in models with changing persistence is under consideration. We introduce the additive and innovative outlier with different outlier detection and removal methods. In a Monte Carlo study, the performance of the tests is investigated and compared in uncontaminated, outlier contaminated and adjusted series. The main finding is that innovative ...