Time Series Modelling with Unobserved Components PDF Download

Author: Matteo M. Pelagatti
Publisher: CRC Press
ISBN: 1482225018
Category : Mathematics
Languages : en
Pages : 275

View

Book Description
Despite the unobserved components model (UCM) having many advantages over more popular forecasting techniques based on regression analysis, exponential smoothing, and ARIMA, the UCM is not well known among practitioners outside the academic community. Time Series Modelling with Unobserved Components rectifies this deficiency by giving a practical o

Author: Siem Jan Koopman
Publisher: Oxford University Press
ISBN: 0191506575
Category : Business & Economics
Languages : en
Pages : 389

View

Book Description
This volume presents original and up-to-date studies in unobserved components (UC) time series models from both theoretical and methodological perspectives. It also presents empirical studies where the UC time series methodology is adopted. Drawing on the intellectual influence of Andrew Harvey, the work covers three main topics: the theory and methodology for unobserved components time series models; applications of unobserved components time series models; and time series econometrics and estimation and testing. These types of time series models have seen wide application in economics, statistics, finance, climate change, engineering, biostatistics, and sports statistics. The volume effectively provides a key review into relevant research directions for UC time series econometrics and will be of interest to econometricians, time series statisticians, and practitioners (government, central banks, business) in time series analysis and forecasting, as well to researchers and graduate students in statistics, econometrics, and engineering.

Author: Andrew C. Harvey
Publisher: Oxford University Press, USA
ISBN: 0199278695
Category : Business & Economics
Languages : en
Pages : 475

View

Book Description
This volume presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with them. The book is intended to give a self-contained presentation of the methods and applicative issues. Harvey has made major contributions to this field and provides substantial introductions throughout the book to form a unified view of the literature. About the Series Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

Author: Andrew C. Harvey
Publisher: Cambridge University Press
ISBN: 9780521405737
Category : Business & Economics
Languages : en
Pages : 574

View

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.

Author: Siem Jan Koopman
Publisher:
ISBN:
Category :
Languages : en
Pages : 34

View

Book Description

Author: Mike West
Publisher: Springer Science & Business Media
ISBN: 1475793650
Category : Mathematics
Languages : en
Pages : 720

View

Book Description
In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.

Author: Jacques J. F. Commandeur
Publisher: OUP Oxford
ISBN: 0191607800
Category : Business & Economics
Languages : en
Pages : 192

View

Book Description
Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.

Author: Marc Nerlove
Publisher:
ISBN:
Category : Business & Economics
Languages : en
Pages : 504

View

Book Description
In this edition which has been reprinted with corrections, Nerlove and his co-authors illustrate techniques of spectral analysis and methods based on parametric models in the analysis of economic time series. The book provides a means and a method for incorporating economic intuition and theory in the formulation of time-series models useful in forecasting, in the formulation and estimation of distributed lag models, and in other applications, such as seasonal adjustment. Analysis of Economic Time Series is a useful primary text for graduate students and an attractive reference for researchers. Key Features * Presents a self-contained treatment of Fourier Analysis and complex variables, as well as Spectral Analysis of time series * Includes a detailed treatment of unobserved-components (UC) models and their time-series properties by means of covariance-generating transforms * Provides the formulation and maximum-likelihood estimation of ARMA and UC models in both time and frequency domains Integrates several topics in time-series analysis: * The formulation and estimation of distributed-lag models of dynamic economic behavior * The application of the techniques of spectral analysis in the study of behavior of economic time series * Unobserved-components models for economic time series and the closely related problem of seasonal adjustment * The complimentarities between time-domain and frequency-domain approaches to the analysis of economic time series * Historical contributions extending from the time of Charles Babbage and the Edinburgh Review to the present * Treats spectral analysis and Box-Jenkins models for an intuitive but rigorous point of view * Shows how these two types of analysis may be synthesized so that they complement one another * Describes a new type of model, based on a superposition of Box-Jenkins models, that captures the essential idea of the unobserved-components models long used in the analysis of economic time series * Applies multiple time-series techniques to the estimation of a novel dynamic model of the US cattle industry

Author: Manfred Deistler
Publisher: Springer Nature
ISBN: 3031132130
Category : Mathematics
Languages : en
Pages : 213

View

Book Description
This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.

Author: C. Planas
Publisher:
ISBN: 9789282815724
Category : Time-series analysis
Languages : en
Pages : 172

View

Book Description
"The general purpose of this textbook is to provide analysts in statistical institutes with a unified view of applied analysis of time series as can be conducted in the framework of linear stochastic models of the ARIMA-type. The issues discussed are modelling and forecasting, filtering, signal extraction and unobserved components analysis, and regression in time series models. The main concern is to help readers in understanding some important tools that progress in statistical theory has made available. Emphasis is thus put on practical aspects, and readers will find implementations of the techniques described in software such as SEATS-TRAMO (see Gomez and Maravall, 1996) and X-12 ARIMA (see Findley et al., 1996)".