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Author : Cristiano Augusto Coelho Fernandes
Publisher :
ISBN 13 :
Total Pages : 492 pages
Book Rating : 4.:/5 (94 download)
Download or read book Non-Gaussian structural time series models written by Cristiano Augusto Coelho Fernandes and published by . This book was released on 1992 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : James Durbin
Publisher : Oxford University Press
ISBN 13 : 019964117X
Total Pages : 369 pages
Book Rating : 4.1/5 (996 download)
Download or read book Time Series Analysis by State Space Methods written by James Durbin and published by Oxford University Press. This book was released on 2012-05-03 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.
Author : James Durbin
Publisher : Oxford University Press
ISBN 13 : 9780198523543
Total Pages : 280 pages
Book Rating : 4.5/5 (235 download)
Download or read book Time Series Analysis by State Space Methods written by James Durbin and published by Oxford University Press. This book was released on 2001-06-21 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: State space time series analysis emerged in the 1960s in engineering, but its applications have spread to other fields. Durbin (statistics, London School of Economics and Political Science) and Koopman (econometrics, Free U., Amsterdam) extol the virtues of such models over the main analytical system currently used for time series data, Box-Jenkins' ARIMA. What distinguishes state space time models is that they separately model components such as trend, seasonal, regression elements and disturbance terms. Part I focuses on traditional and new techniques based on the linear Gaussian model. Part II presents new material extending the state space model to non-Gaussian observations. c. Book News Inc.
Author : Andrew C. Harvey
Publisher : Cambridge University Press
ISBN 13 : 9780521405737
Total Pages : 574 pages
Book Rating : 4.4/5 (57 download)
Download or read book Forecasting, Structural Time Series Models and the Kalman Filter written by Andrew C. Harvey and published by Cambridge University Press. This book was released on 1990 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.
Author : Andrew C. Harvey
Publisher : Cambridge University Press
ISBN 13 : 1107717140
Total Pages : 578 pages
Book Rating : 4.1/5 (77 download)
Download or read book Forecasting, Structural Time Series Models and the Kalman Filter written by Andrew C. Harvey and published by Cambridge University Press. This book was released on 1990-02-22 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.
Author : N. Balakrishna
Publisher : Springer Nature
ISBN 13 : 9811681627
Total Pages : 238 pages
Book Rating : 4.8/5 (116 download)
Download or read book Non-Gaussian Autoregressive-Type Time Series written by N. Balakrishna and published by Springer Nature. This book was released on 2022-01-27 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties.
Author : Leanna Marisa Tedesco
Publisher :
ISBN 13 :
Total Pages : 274 pages
Book Rating : 4.:/5 (221 download)
Download or read book Non-Gaussian First-order Autoregressive Time Series Models written by Leanna Marisa Tedesco and published by . This book was released on 1995 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michael Benjamin
Publisher :
ISBN 13 :
Total Pages : 344 pages
Book Rating : 4.:/5 (595 download)
Download or read book A Generalized Family of Time Series Models for Non-Gaussian Data written by Michael Benjamin and published by . This book was released on 1999 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Andrew G. Bruce
Publisher :
ISBN 13 :
Total Pages : 48 pages
Book Rating : 4.:/5 (17 download)
Download or read book Non-Gaussian Season Adjustment written by Andrew G. Bruce and published by . This book was released on 1992 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study compares X-12-ARIMA and MING, two new seasonal adjustment methods designed to handle outliers and structural changes in a time series. X-12-ARIMA is a successor to the X-11-ARIMA seasonal adjustment method, and is being developed at the U.S. Bureau of the Census (Findley et al. (1988)). MING is a "Mixture based Non-Gaussian" method for seasonal adjustment using time series structural models. It was developed for this study based on methodology proposed by Kitagawa (1990).
Author : Genshiro Kitagawa
Publisher : CRC Press
ISBN 13 : 1584889225
Total Pages : 315 pages
Book Rating : 4.5/5 (848 download)
Download or read book Introduction to Time Series Modeling written by Genshiro Kitagawa and published by CRC Press. This book was released on 2010-04-21 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very im
Author : Eric Jondeau
Publisher : Springer Science & Business Media
ISBN 13 : 1846286964
Total Pages : 541 pages
Book Rating : 4.8/5 (462 download)
Download or read book Financial Modeling Under Non-Gaussian Distributions written by Eric Jondeau and published by Springer Science & Business Media. This book was released on 2007-04-05 with total page 541 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.
Author : Genshiro Kitagawa
Publisher : CRC Press
ISBN 13 : 0429582625
Total Pages : 262 pages
Book Rating : 4.4/5 (295 download)
Download or read book Introduction to Time Series Modeling with Applications in R written by Genshiro Kitagawa and published by CRC Press. This book was released on 2020-08-10 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the first edition: [This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. ... [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space framework. –Statistics in Medicine What distinguishes this book from comparable introductory texts is the use of state-space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing, including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters. –MAA Reviews Introduction to Time Series Modeling with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series. The second edition makes it possible for readers to reproduce examples in this book by using the freely available R package TSSS to perform computations for their own real-world time series problems. This book employs the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle filter as convenient tools for recursive estimation for state-space models. Further, it also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models and model selection by AIC. Along with the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models. About the Author: Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.
Author : Tevfik Aktekin
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book A Family of Multivariate Non-Gaussian Time Series Models written by Tevfik Aktekin and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this article, we propose a class of multivariate non-Gaussian time series models which include dynamic versions of many well-known distributions and consider their Bayesian analysis. A key feature of our proposed model is its ability to account for correlations across time as well as across series (contemporary) via a common random environment. The proposed modeling approach yields analytically tractable dynamic marginal likelihoods, a property not typically found outside of linear Gaussian time series models. These dynamic marginal likelihoods can be tied back to known static multivariate distributions such as the Lomax, generalized Lomax, and the multivariate Burr distributions. The availability of the marginal likelihoods allows us to develop efficient estimation methods for various settings using Markov chain Monte Carlo as well as sequential Monte Carlo methods. Our approach can be considered to be a multivariate generalization of commonly used univariate non-Gaussian class of state space models. To illustrate our methodology, we use simulated data examples and a real application of multivariate time series for modeling the joint dynamics of stochastic volatility in financial indexes, the VIX and VXN.
Author : J. Durbin
Publisher :
ISBN 13 :
Total Pages : 52 pages
Book Rating : 4.:/5 (415 download)
Download or read book Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives written by J. Durbin and published by . This book was released on 1998 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Nhu Dinh Le
Publisher :
ISBN 13 :
Total Pages : 338 pages
Book Rating : 4.:/5 (242 download)
Download or read book Modeling and Bootstrapping for Non-Gaussian Time Series written by Nhu Dinh Le and published by . This book was released on 1990 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Non gaussian state space models for count data: the durbin and koopman methodology written by and published by . This book was released on 1902 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: O objetivo desta tese é o de apresentar e investigar a metodologia de Durbin e Koopman (DK) usada para estimar o espaço de estado de modelos de séries temporais não-Gaussianos, dentro do contexto de modelos estruturais. A abordagem de DK está baseada na avaliação da verossimilhança usando uma eficiente simulação de Monte Carlo, por meio de amostragem por importância e técnicas de redução de variância, tais como variáveis antitéticas e variáveis de controle. Ela também integra conhecidas técnicas existentes no caso Gaussiano tais como o Filtro de Kalman Siavizado e o algoritmo de simulação suavizada. Uma vez que os hiperparâmetros do modelo são estimados, o estado, que contém as componentes do modelo, é estimado pela avaliação da moda a posteriori. Propomos então aproximações para avaliar a média e a variância da distribuição preditiva. São consideradas aplicações usando o modelo de Poisson.
Author : Chris Chatfield
Publisher : CRC Press
ISBN 13 : 1420036203
Total Pages : 281 pages
Book Rating : 4.4/5 (2 download)
Download or read book Time-Series Forecasting written by Chris Chatfield and published by CRC Press. This book was released on 2000-10-25 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space
