Author: Cristiano Augusto Coelho FernandesPublisher:
ISBN:
Category :
Languages : en
Pages : 492
Book Description
Non-Gaussian structural time series models
Author: Cristiano Augusto Coelho FernandesPublisher:
ISBN:
Category :
Languages : en
Pages : 492
Book Description
Time Series Analysis by State Space Methods
Author: James DurbinPublisher: Oxford University Press
ISBN: 019964117X
Category : Business & Economics
Languages : en
Pages : 369
Book Description
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.
Time Series Analysis by State Space Methods
Author: James DurbinPublisher: Oxford University Press
ISBN: 9780198523543
Category : Business & Economics
Languages : en
Pages : 280
Book Description
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.
Forecasting, Structural Time Series Models and the Kalman Filter
Author: Andrew C. HarveyPublisher: Cambridge University Press
ISBN: 9780521405737
Category : Business & Economics
Languages : en
Pages : 574
Book Description
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.
Forecasting, Structural Time Series Models and the Kalman Filter
Author: Andrew C. HarveyPublisher: Cambridge University Press
ISBN: 1107717140
Category : Business & Economics
Languages : en
Pages : 578
Book Description
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.
Non-Gaussian Autoregressive-Type Time Series
Author: N. BalakrishnaPublisher: Springer Nature
ISBN: 9811681627
Category : Mathematics
Languages : en
Pages : 238
Book Description
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.
Non-Gaussian First-order Autoregressive Time Series Models
Author: Leanna Marisa TedescoPublisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 274
Book Description
A Generalized Family of Time Series Models for Non-Gaussian Data
Author: Michael BenjaminNon-Gaussian Season Adjustment
Author: Andrew G. BrucePublisher:
ISBN:
Category : Computer simulation
Languages : en
Pages : 48
Book Description
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).
Financial Modeling Under Non-Gaussian Distributions
Author: Eric JondeauPublisher: Springer Science & Business Media
ISBN: 1846286964
Category : Mathematics
Languages : en
Pages : 541
Book Description
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.