Browsing by Subject "Regulated time series"

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Open Access
Improving inference in integration and cointegration tests
(2016-05) Eroğlu, Burak Alparslan
In this thesis, I address three di erent problems in unit root and cointegration models and I propose new methods to improve inference in testing procedures for these models. Two of these problems are related to unit root tests. First one is so-called nonstationary volatility issue, which causes severe size distortions in standard unit root tests. I try to resolve this problem with a nonparametric technique introduced rst by Nielsen (2009). Second, I investigate the unit root testing under regulation, which constraints a time series process on a given interval. In this case, standard tests frequently fail to detect the presence of nonstationarity. I employ a similar methodology as in rst part and provide correct inference in unit root testing for regulated series. The nal problem is related to cointegration models. In these models, if innovations of the system are contaminated by MA type negative serial correlation, cointegration tests spuriously rejects the true null hypothesis. Combining wavelet theory and Nielsen's (2010) variance ratio testing procedure, I manage to reduce the impact of the problematic innovations on cointegration test. All three methods share the common feature of being nonparametric in sense that they do not require any regression or kernel type correction to handle serial correlation.
Open Access
Regulated fractionally integrated processes
(Wiley-Blackwell Publishing Ltd., 2013) Trokic, M.
Regulated (bounded) integrated time series are of significant practical importance and a recent development in the time series literature. Although regulated integrated series are characterized by asymptotic distributions that differ substantially from their unregulated counterparts, most inferential exercises continue to be performed with complete disregard for this potential feature of time series data. To date, only Cavaliere (2005) and Cavaliere and Xu (2011) have attempted to develop a theory for regulated integrated time series, particularly in the context of unit root testing. Unfortunately, no such theory has been developed for regulated fractionally integrated series, which are particularly important in financial time series and also in some unit root testing literature. This article achieves just this: it establishes a framework for regulated fractionally integrated processes and develops their functional central limit distributions. In addition, this article presents some simulation evidence and discusses several algorithms for obtaining the limiting distributions for these processes.