Download or read book Quantile Inference and Change Point Test Under Time Series Non stationarity written by Weichi Wu and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Empirical Likelihood and Quantile Methods for Time Series written by Yan Liu and published by Springer. This book was released on 2018-12-05 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the estimation motivated by minimizing quantile prediction error without assumption of true model. It provides the reader with a new horizon for understanding the prediction problem that occurs in time series modeling and a contemporary approach of hypothesis testing by the generalized empirical likelihood method. Nonparametric aspects of the methods proposed in this book also satisfactorily address economic and financial problems without imposing redundantly strong restrictions on the model, which has been true until now. Dealing with infinite variance processes makes analysis of economic and financial data more accurate under the existing results from the demonstrative research. The scope of applications, however, is expected to apply to much broader academic fields. The methods are also sufficiently flexible in that they represent an advanced and unified development of prediction form including multiple-point extrapolation, interpolation, and other incomplete past forecastings. Consequently, they lead readers to a good combination of efficient and robust estimate and test, and discriminate pivotal quantities contained in realistic time series models.
Download or read book Non stationary Time Series Analysis a Nonlinear Systems Approach written by Zhou Zhou and published by . This book was released on 2009 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, technological innovations have made it possible to collect a massive amount of data with complex structure over a relatively long period of time. For such data, many empirical evidences have shown that the traditional time series analysis tools for stationary processes are insufficient. As a consequence, there have been great demands and interests for theoretical and methodological advancements for non-stationary time series analysis. In this thesis, we shall propose a general class of non-stationary time series models, which we call locally stationary time series, and discuss the consequences of such modelling. In particular, inferences of quantile curves and conditional mean functions of such processes will be discussed in detail. Numerous real data examples shows the applicability of our theory and methodology in fields such as environmental sciences and financial econometrics.
Download or read book Non linear and Non stationary Time Series Analysis written by Maurice Bertram Priestley and published by . This book was released on 1988 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Testing Statistical Hypotheses of Equivalence and Noninferiority written by Stefan Wellek and published by CRC Press. This book was released on 2010-06-24 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: While continuing to focus on methods of testing for two-sided equivalence, Testing Statistical Hypotheses of Equivalence and Noninferiority, Second Edition gives much more attention to noninferiority testing. It covers a spectrum of equivalence testing problems of both types, ranging from a one-sample problem with normally distributed observations
Download or read book Learning in Non Stationary Environments written by Moamar Sayed-Mouchaweh and published by Springer Science & Business Media. This book was released on 2012-04-13 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
Download or read book Three Essays on Time Series Quantile Regression written by Yini Wang and published by . This book was released on 2012 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation considers quantile regression models with nonstationary or nearly nonstationary time series. The first chapter outlines the thesis and discusses its theoretical and empirical contributions. The second chapter studies inference in quantile regressions with cointegrated variables allowing for multiple structural changes. The unknown break dates and regression coefficients are estimated jointly and consistently. The conditional quantile estimator has a nonstandard limit distribution. A fully modified estimator is proposed to remove the second-order bias and nuisance parameters and the resulting limit distribution is mixed normal. A simulation study shows that the fully modified quantile estimator has good finite sample properties. The model is applied to stock index data from the emerging markets of China and several mature markets. Financial market integration is found in some quantiles of the Chinese stock indices. The third chapter considers predictive quantile regression with a nearly integrated regressor. We derive nonstandard distributions for the quantile regression estimator and t-statistic in terms of functionals of diffusion processes. The critical values are found to depend on both the quantile of interest and the local-to-unity parameter, which is not consistently estimable. Based on these critical values, we propose a valid Bonferroni bounds test for quantile predictability with persistent regressors. We employ this new methodology to test the ability of many commonly employed and highly persistent regressors, such as the dividend yield, earnings price ratio, and T-bill rate, to predict the median, shoulders, and tails of the stock return distribution. Chapter Four proposes a cumulated sum (CUSUM) test for the null hypothesis of quantile cointegration. A fully modified quantile estimator is adopted for serial correlation and endogeneity corrections. The CUSUM statistic is composed of the partial sums of the residuals from the fully modified quantile regression. Under the null, the test statistic converges to a functional of Brownian motions. In the application to U.S. interest rates of different maturities, evidence in favor of the expectations hypothesis for the term structure is found in the central part of the distributions of the Treasury bill rate and financial commercial paper rate, but in the tails of the constant maturity rate distribution.
Download or read book Extreme Values in Finance Telecommunications and the Environment written by Barbel Finkenstadt and published by CRC Press. This book was released on 2003-07-28 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because of its potential to ...predict the unpredictable,... extreme value theory (EVT) and methodology is currently receiving a great deal of attention from statistical and mathematical researchers. This book brings together world-recognized authorities in their respective fields to provide expository chapters on the applications, use, and theory
Download or read book Identifying and Modeling Non stationarity and Non linearity in Time Series Data written by Brian Jersky and published by . This book was released on 1992 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Statistics written by Petr Mandl and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: In particular up-to-date-information is presented in detection of systematic changes, in series of observation, in robust regression analysis, in numerical empirical processes and in related areas of actuarial sciences.
Download or read book Inference for Single and Multiple Change Points in Time Series written by Venkata Jandhyala and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The article reviews methods of inference for single and multiple change-points in time series, when data are of retrospective (off-line) type. The inferential methods reviewed for a single change-point in time series include likelihood, Bayes, Bayes-type and some relevant non-parametric methods. Inference for multiple change-points requires methods that can handle large data sets and can be implemented efficiently for estimating the number of change-points as well as their locations. Our review in this important area focuses on some of the recent advances in this direction. Greater emphasis is placed on multivariate data while reviewing inferential methods for a single change-point in time series. Throughout the article, more attention is paid to estimation of unknown change-point(s) in time series, and this is especially true in the case of multiple change-points. Some specific data sets for which change-point modelling has been carried out in the literature are provided as illustrative examples under both single and multiple change-point scenarios.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1977 with total page 1006 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonlinear Time Series Analysis written by Ruey S. Tsay and published by John Wiley & Sons. This book was released on 2018-09-14 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.
Download or read book Non linear and Non stationary Time Series Analysis written by Maurice B. Priestley and published by . This book was released on 1989 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stable Paretian Models in Finance written by Svetlozar T. Rachev and published by . This book was released on 2000-06-15 with total page 886 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors reconsider the problem of parametrically specifying distribution suitable for asset-return models. They describe alternative distributions, showing how they can be estimated and applied to stock-index and exchange-rate data. The implications for options pricing are also investigated.
Download or read book A Change in Level of a Non stationary Time Series written by G. E. P. Box and published by . This book was released on 1964 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Suppose that observations zsubt of a time series are available at equally spaced time intervals. The authors consider the problem of making inferences about a possible shift in level of the series associated with the occurrance of an event E at some particular time. For example, the observations might be of some economic indicator and one might suspect a change in level to occur in a particular interval because of a change in fiscal policy. Alternatively, zsubt could be the daily output of a chemical process and the event E might be a change in the supplier of raw material.
Download or read book Quantile Self Exciting Threshold Autoregressive Time Series Models written by Yuzhi Cai and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we present a Bayesian approach to quantile self-exciting threshold autoregressive time series models. The simulation work shows that the method can deal very well with non-stationary time series with very large, but not necessarily symmetric, variations. The methodology has also been applied to the growth rate of US real GNP data and some interesting results have been obtained.
