Download or read book Asymptotic Inference for Nearly Non Stationary Time Series written by Isabel Llatas and published by . This book was released on 1987 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Inference of Nearly Nonstationary Complex valued AR 1 Processes written by J. Kormos and published by . This book was released on 1993 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Theory of Statistical Inference for Time Series written by Masanobu Taniguchi and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual AR, MA, and ARMA processes. A wide variety of stochastic processes, including non-Gaussian linear processes, long-memory processes, nonlinear processes, non-ergodic processes and diffusion processes are described. The authors discuss estimation and testing theory and many other relevant statistical methods and techniques.
Download or read book Studies in Asymptotic and Finite Sample Inference of Nonstationary and Nearly Nonstationary Autoregressive Models written by Juha Antti Ahtola and published by . This book was released on 1983 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Athens Conference on Applied Probability and Time Series Analysis written by P.M. Robinson and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Athens Conference on Applied Probability and Time Series in 1995 brought together researchers from across the world. The published papers appear in two volumes. Volume II presents papers on time series analysis, many of which were contributed to a meeting in March 1995 partly in honour of E.J. Hannan. The initial paper by P.M. Robinson discusses Ted Hannan's researches and their influence on current work in time series analysis. Other papers discuss methods for finite parameter Gaussian models, time series with infinite variance or stable marginal distribution, frequency domain methods, long range dependent processes, nonstationary processes, and nonlinear time series. The methods presented can be applied in a number of fields such as statistics, applied mathematics, engineering, economics and ecology. The papers include many of the topics of current interest in time series analysis and will be of interest to a wide range of researchers.
Download or read book Asymptotic Nonparametric Statistical Analysis of Stationary Time Series written by Daniil Ryabko and published by Springer. This book was released on 2019-03-07 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus a rather attractive assumption to base statistical analysis on, especially for problems for which less general qualitative assumptions, such as independence or finite memory, clearly fail. However, it has long been considered too general to be able to make statistical inference. One of the reasons for this is that rates of convergence, even of frequencies to the mean, are not available under this assumption alone. Recently, it has been shown that, while some natural and simple problems, such as homogeneity, are indeed provably impossible to solve if one only assumes that the data is stationary (or stationary ergodic), many others can be solved with rather simple and intuitive algorithms. The latter include clustering and change point estimation among others. In this volume these results are summarize. The emphasis is on asymptotic consistency, since this the strongest property one can obtain assuming stationarity alone. While for most of the problem for which a solution is found this solution is algorithmically realizable, the main objective in this area of research, the objective which is only partially attained, is to understand what is possible and what is not possible to do for stationary time series. The considered problems include homogeneity testing (the so-called two sample problem), clustering with respect to distribution, clustering with respect to independence, change point estimation, identity testing, and the general problem of composite hypotheses testing. For the latter problem, a topological criterion for the existence of a consistent test is presented. In addition, a number of open problems is presented.
Download or read book Estimation and Inference for High Dimensional Time Series written by Danna Zhang and published by . This book was released on 2017 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a well-developed asymptotic theory for sample means and sample second-order statistics of low dimensional stationary processes. However, many important problems on their asymptotic behaviors are still unanswered for time series which can be high-dimensional, nonstationary and non-Gaussian.
Download or read book Asymptotic Inference of Nonstationary Autoregressive Processes written by Ngai Hang Chan and published by . This book was released on 1985 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Time Series Theory and Methods written by Peter J. Brockwell and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: We have attempted in this book to give a systematic account of linear time series models and their application to the modelling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the techniques. Both time and frequency domain methods are discussed but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It has been used both at the M. S. level, emphasizing the more practical aspects of modelling, and at the Ph. D. level, where the detailed mathematical derivations of the deeper results can be included. Distinctive features of the book are the extensive use of elementary Hilbert space methods and recursive prediction techniques based on innovations, use of the exact Gaussian likelihood and AIC for inference, a thorough treatment of the asymptotic behavior of the maximum likelihood estimators of the coefficients of univariate ARMA models, extensive illustrations of the tech niques by means of numerical examples, and a large number of problems for the reader. The companion diskette contains programs written for the IBM PC, which can be used to apply the methods described in the text.
Download or read book Asymptotic Inference for Nonstationary Fractionally Integrated Processes written by Juan José Dolado and published by . This book was released on 1999 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Likelihood based Inference in Cointegrated Vector Autoregressive Models written by Søren Johansen and published by Oxford University Press, USA. This book was released on 1995 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is concerned with the statistical analysis of multivariate systems of non-stationary time series of type I. It applies the concepts of cointegration and common trends in the framework of the Gaussian vector autoregressive model.
Download or read book Macroeconometrics and Time Series Analysis written by Steven Durlauf and published by Springer. This book was released on 2016-04-30 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.
Download or read book Almost All About Unit Roots written by In Choi and published by Cambridge University Press. This book was released on 2015-05-12 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many economic theories depend on the presence or absence of a unit root for their validity, making familiarity with unit roots extremely important to econometric and statistical theory. This book introduces the literature on unit roots in a comprehensive manner to empirical and theoretical researchers in economics and other areas.
Download or read book Asymptotic Inference for Locally Stationary Processes written by Inder Rafael Tecuapetla Gomez and published by . This book was released on 2013 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of locally stationary processes contains theory and methods about a class of processes that describe random phenomena whose fluctuations occur both in time and space. We consider three aspects of locally stationary processes that have not been explore in the already vast literature on these nonstationary processes. We begin by studying the asymptotic efficiency of simple hypotheses tests via large deviation principles. We establish the analogues of classic results such as Stein's lemma, Chernoff bound and the more general Hoeffding bound. These results are based on a large deviation principle for the log-likelihood ratio test statistic between two locally stationary Gaussian processes which is obtained and presented in the first chapter. In the second chapter we consider the Bayesian estimation of two parameters of a locally stationary process: trend and time-varying spectral density functions, respectively. Under smoothness conditions on the latter function, we obtain the asymptotic normality and efficiency, with respect to a broad class of loss functions, of Bayesian estimators. In passing we also show the asymptotic equivalence between Bayesian estimators and the maximum likelihood estimate. Our concluding fourth chapter explores the time-varying spectral density estimation problem from the point of view of Le Cam's theory of statistical experiments. We establish that the estimation of a time-varying spectral density function can be asymptotically construed as a white noise problem with drift. This result is based on Le Cam's connection theorem.
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 Time Series Analysis written by Katsuto Tanaka and published by John Wiley & Sons. This book was released on 2017-04-03 with total page 903 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reflects the developments and new directions in the field since the publication of the first successful edition and contains a complete set of problems and solutions This revised and expanded edition reflects the developments and new directions in the field since the publication of the first edition. In particular, sections on nonstationary panel data analysis and a discussion on the distinction between deterministic and stochastic trends have been added. Three new chapters on long-memory discrete-time and continuous-time processes have also been created, whereas some chapters have been merged and some sections deleted. The first eleven chapters of the first edition have been compressed into ten chapters, with a chapter on nonstationary panel added and located under Part I: Analysis of Non-fractional Time Series. Chapters 12 to 14 have been newly written under Part II: Analysis of Fractional Time Series. Chapter 12 discusses the basic theory of long-memory processes by introducing ARFIMA models and the fractional Brownian motion (fBm). Chapter 13 is concerned with the computation of distributions of quadratic functionals of the fBm and its ratio. Next, Chapter 14 introduces the fractional Ornstein–Uhlenbeck process, on which the statistical inference is discussed. Finally, Chapter 15 gives a complete set of solutions to problems posed at the end of most sections. This new edition features: • Sections to discuss nonstationary panel data analysis, the problem of differentiating between deterministic and stochastic trends, and nonstationary processes of local deviations from a unit root • Consideration of the maximum likelihood estimator of the drift parameter, as well as asymptotics as the sampling span increases • Discussions on not only nonstationary but also noninvertible time series from a theoretical viewpoint • New topics such as the computation of limiting local powers of panel unit root tests, the derivation of the fractional unit root distribution, and unit root tests under the fBm error Time Series Analysis: Nonstationary and Noninvertible Distribution Theory, Second Edition, is a reference for graduate students in econometrics or time series analysis. Katsuto Tanaka, PhD, is a professor in the Faculty of Economics at Gakushuin University and was previously a professor at Hitotsubashi University. He is a recipient of the Tjalling C. Koopmans Econometric Theory Prize (1996), the Japan Statistical Society Prize (1998), and the Econometric Theory Award (1999). Aside from the first edition of Time Series Analysis (Wiley, 1996), Dr. Tanaka had published five econometrics and statistics books in Japanese.
Download or read book Forecasting Unstable and Non stationary Time Series written by Carlo Grillenzoni and published by . This book was released on 1993 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
