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 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 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 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 Asymptotic Optimal Inference for Non ergodic Models written by I. V. Basawa and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph contains a comprehensive account of the recent work of the authors and other workers on large sample optimal inference for non-ergodic models. The non-ergodic family of models can be viewed as an extension of the usual Fisher-Rao model for asymptotics, referred to here as an ergodic family. The main feature of a non-ergodic model is that the sample Fisher information, appropriately normed, converges to a non-degenerate random variable rather than to a constant. Mixture experiments, growth models such as birth processes, branching processes, etc. , and non-stationary diffusion processes are typical examples of non-ergodic models for which the usual asymptotics and the efficiency criteria of the Fisher-Rao-Wald type are not directly applicable. The new model necessitates a thorough review of both technical and qualitative aspects of the asymptotic theory. The general model studied includes both ergodic and non-ergodic families even though we emphasise applications of the latter type. The plan to write the monograph originally evolved through a series of lectures given by the first author in a graduate seminar course at Cornell University during the fall of 1978, and by the second author at the University of Munich during the fall of 1979. Further work during 1979-1981 on the topic has resolved many of the outstanding conceptual and technical difficulties encountered previously. While there are still some gaps remaining, it appears that the mainstream development in the area has now taken a more definite shape.
Download or read book Using Difference Based Methods for Inference in Regression With Fractionally Integrated Processes written by Wen-Jen Tsay and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper suggests a difference-based method for inference in the regression model involving fractionally integrated processes. Under suitable regularity conditions, our method can effectively deal with the inference problems associated with the regression model consisting of non-stationary, stationary and intermediate memory regressors, simultaneously. Although the difference-based method provides a very flexible modeling framework for empirical studies, the implementation of this method is extremely easy, because it completely avoids the difficult problems of choosing a kernel function, a bandwidth parameter, or an autoregressive lag length for the long-run variance estimation. The asymptotic local power of our method is investigated with a sequence of local data-generating processes (DGP) in what Davidson and MacKinnon [Canadian Journal of Economics. (1985) Vol. 18, pp. 3857] call regression direction. The simulation results indicate that the size control of our method is excellent even when the sample size is only 100, and the pattern of power performance is highly consistent with the theoretical finding from the asymptotic local power analysis conducted in this paper.
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 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 A Companion to Theoretical Econometrics written by Badi H. Baltagi and published by John Wiley & Sons. This book was released on 2008-04-15 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Companion to Theoretical Econometrics provides a comprehensive reference to the basics of econometrics. This companion focuses on the foundations of the field and at the same time integrates popular topics often encountered by practitioners. The chapters are written by international experts and provide up-to-date research in areas not usually covered by standard econometric texts. Focuses on the foundations of econometrics. Integrates real-world topics encountered by professionals and practitioners. Draws on up-to-date research in areas not covered by standard econometrics texts. Organized to provide clear, accessible information and point to further readings.
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 Recent Developments in Nonparametric Inference and Probability written by and published by IMS. This book was released on 2006 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Optimal Inference for Non ergodic Models written by Ishwar V. Basawa and published by . This book was released on 1983 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2007 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt: A scientific and educational journal not only for professional statisticians but also for economists, business executives, research directors, government officials, university professors, and others who are seriously interested in the application of statistical methods to practical problems, in the development of more useful methods, and in the improvement of basic statistical data.
Download or read book ASYMPTOTIC INFERENCE FOR STOCHASTIC PROCESSES written by B.L.S. PRAKASA RAO and published by . This book was released on 1979 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Time Series Analysis written by Wilfredo Palma and published by John Wiley & Sons. This book was released on 2016-04-28 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley.
Download or read book Statistical Inference for Financial Engineering written by Masanobu Taniguchi and published by Springer Science & Business Media. This book was released on 2014-03-26 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e.g., discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc., which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering. This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.
