Topics in Nonstationary Time Series Analysis

Several interesting applications in areas such as neuroscience, economics, finance and seismology have led to the collection nonstationary time series data wherein the statistical properties of the observed process change across time. The analysis of nonstationary time series data is an important and challenging task with useful applications. In comparison to stationarity, modeling temporal dependence in nonstationary time series is more non-trivial, and numerous methods have been proposed to tackle this problem. Stationarity in time series is more coveted than nonstationarity and many of the existing techniques attempt to transform the problem of nonstationarity to a stationary time series setting. Change point detection is one such method that attempts to find time points wherein the statistical properties of the time series changed. We develop a nonparametric method to detect multiple change points in multivariate piecewise stationary processes when the locations and number of change points are unknown. Based on a test statistic that measures differences in the spectral density matrices through the L₂ norm, we sequentially identify points of local maxima in the test statistic and test for the significance of each of them being change points. In addition, the components responsible for the change in the covariance structure at each detected change point are identified. The asymptotic properties of the test for significant change points under the null and alternative hypothesis are derived. Another related method for handling nonstationarity is the recent technique of stationary subspace analysis (SSA) that aims at finding linear transformations of nonstationary processes that are stationary. We propose an SSA procedure for general multivariate second-order nonstationary processes. It relies on the asymptotic uncorrelatedness of the discrete Fourier transform of a stationary time series to define a measure of departure from stationarity; it is then minimized to find the stationary subspace. The dimension of the subspace is estimated using a sequential testing procedure and its asymptotic properties are discussed. We illustrate the broader applicability and better performance of our method in comparison to existing SSA methods through simulations and discuss an application in neuroeconomics. Here we apply our method to filter out noise in EEG brain signals from an economic choice task experiment. This improves prediction performance and more importantly reduces the number of trials needed from individuals in neuroeconomic experiments thereby aligning with the principle of simple and controlled designs in experimental and behavioral economics

Topics in Nonstationary Time Series Analysis

Several interesting applications in areas such as neuroscience, economics, finance and seismology have led to the collection nonstationary time series data wherein the statistical properties of the observed process change across time. The analysis of nonstationary time series data is an important and challenging task with useful applications. In comparison to stationarity, modeling temporal dependence in nonstationary time series is more non-trivial, and numerous methods have been proposed to tackle this problem. Stationarity in time series is more coveted than nonstationarity and many of the existing techniques attempt to transform the problem of nonstationarity to a stationary time series setting. Change point detection is one such method that attempts to find time points wherein the statistical properties of the time series changed. We develop a nonparametric method to detect multiple change points in multivariate piecewise stationary processes when the locations and number of change points are unknown. Based on a test statistic that measures differences in the spectral density matrices through the L₂ norm, we sequentially identify points of local maxima in the test statistic and test for the significance of each of them being change points. In addition, the components responsible for the change in the covariance structure at each detected change point are identified. The asymptotic properties of the test for significant change points under the null and alternative hypothesis are derived. Another related method for handling nonstationarity is the recent technique of stationary subspace analysis (SSA) that aims at finding linear transformations of nonstationary processes that are stationary. We propose an SSA procedure for general multivariate second-order nonstationary processes. It relies on the asymptotic uncorrelatedness of the discrete Fourier transform of a stationary time series to define a measure of departure from stationarity; it is then minimized to find the stationary subspace. The dimension of the subspace is estimated using a sequential testing procedure and its asymptotic properties are discussed. We illustrate the broader applicability and better performance of our method in comparison to existing SSA methods through simulations and discuss an application in neuroeconomics. Here we apply our method to filter out noise in EEG brain signals from an economic choice task experiment. This improves prediction performance and more importantly reduces the number of trials needed from individuals in neuroeconomic experiments thereby aligning with the principle of simple and controlled designs in experimental and behavioral economics

Unit Roots and Structural Breaks: A Survey of the Literature

Since Perron (1989) the time series literature has emphasised the importance of testing for structural breaks in typical economic data sets and pronounced the implications of structural breaks when testing for unit root processes. In this paper we survey recent developments in testing for unit roots taking account of possible structural breaks. In doing so we discuss the distinction between taking structural break dates as exogenously determined, an approach initially adopted in the literature, and endogenously testing break dates. That is, we differentiate between testing for breaks when the break date is known and when it is assumed to be unknown. Also important is the distinction between discrete breaks and gradual breaks. Additionally we describe tests for both single and multiple breaks and discuss some of the pitfalls of the latter.

Reaction times of monitoring schemes for ARMA time series

This paper is concerned with deriving the limit distributions of stopping times devised to sequentially uncover structural breaks in the parameters of an autoregressive moving average, ARMA, time series. The stopping rules are defined as the first time lag for which detectors, based on CUSUMs and Page's CUSUMs for residuals, exceed the value of a prescribed threshold function. It is shown that the limit distributions crucially depend on a drift term induced by the underlying ARMA parameters. The precise form of the asymptotic is determined by an interplay between the location of the break point and the size of the change implied by the drift. The theoretical results are accompanied by a simulation study and applications to electroencephalography, EEG, and IBM data. The empirical results indicate a satisfactory behavior in finite samples.Comment: Published at http://dx.doi.org/10.3150/14-BEJ604 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

A METHODOLOGY FOR DETECTING BREAKS IN THE MEAN AND COVARIANCE STRUCTURE OF TIME SERIES

Some structural break techniques defined in the time and frequency domains are presented to explore, at the same time, the empirical evidence of the mean and covariance instability by uncovering regime-shifts in some inflation series. To that effect, we pursue a methodology that combines two approaches; the first is defined in the time domain and is designed to detect mean-shifts, and the second is defined in the frequency domain and is adopted to study the instability problem of the covariance function of the series. The proposed methodology has a double interest since, besides the detection of regime-shifts occasioned in the covariance structure of the series, it allows taking into account the presence of mean-shifts in this series. Note that unlike the works existing in the literature which often adopt a single technique to study the break identification problem, our methodology combines two approaches, parametric and nonparametric, to examine this problem.Structural change, mean and variance shifts, parametric and nonparametric approaches.