State Space And Unobserved Component Models PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download State Space And Unobserved Component Models PDF full book. Access full book title State Space And Unobserved Component Models.

State Space and Unobserved Component Models

State Space and Unobserved Component Models
Author: James Durbin
Publisher: Cambridge University Press
Total Pages: 398
Release: 2004-06-10
Genre: Business & Economics
ISBN: 9780521835954

Download State Space and Unobserved Component Models Book in PDF, ePub and Kindle

A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.


Time Series Modelling with Unobserved Components

Time Series Modelling with Unobserved Components
Author: Matteo M. Pelagatti
Publisher: CRC Press
Total Pages: 275
Release: 2015-07-28
Genre: Mathematics
ISBN: 1482225018

Download Time Series Modelling with Unobserved Components Book in PDF, ePub and Kindle

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


State-Space Models

State-Space Models
Author: Yong Zeng
Publisher: Springer Science & Business Media
Total Pages: 358
Release: 2013-08-15
Genre: Business & Economics
ISBN: 1461477891

Download State-Space Models Book in PDF, ePub and Kindle

State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations. The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in Nonlinear State-Space Models. The second part focuses on the application of Linear State-Space Models in Macroeconomics and Finance. The third part deals with Hidden Markov Models, Regime Switching and Mathematical Finance and the fourth part is on Nonlinear State-Space Models for High Frequency Financial Data. The book will appeal to graduate students and researchers studying state-space modeling in economics, statistics, and mathematics, as well as to finance professionals.


Macroeconometrics and Time Series Analysis

Macroeconometrics and Time Series Analysis
Author: Steven Durlauf
Publisher: Springer
Total Pages: 417
Release: 2016-04-30
Genre: Business & Economics
ISBN: 0230280838

Download Macroeconometrics and Time Series Analysis Book in PDF, ePub and Kindle

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.


An Introduction to State Space Time Series Analysis

An Introduction to State Space Time Series Analysis
Author: Jacques J. F. Commandeur
Publisher: OUP Oxford
Total Pages: 192
Release: 2007-07-19
Genre: Business & Economics
ISBN: 0191607800

Download An Introduction to State Space Time Series Analysis Book in PDF, ePub and Kindle

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.


Readings in Unobserved Components Models

Readings in Unobserved Components Models
Author: Andrew Harvey
Publisher: OUP Oxford
Total Pages: 472
Release: 2005-04-07
Genre: Business & Economics
ISBN: 019151554X

Download Readings in Unobserved Components Models Book in PDF, ePub and Kindle

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. - ;This book 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. It contains four parts, three of which concern recent theoretical developments in classical and Bayesian estimation of linear, nonlinear, and non Gaussian UC models, signal extraction and testing, and one is devoted to selected econometric applications. The first part focuses on the linear state space model; the readings provide insight on prediction theory, signal extraction, and likelihood inference for non stationary and non invertible processes, diagnostic checking, and the use of state space methods for spline smoothing. Part II deals with applications of linear UC models to various estimation problems concerning economic time series, such as trend-cycle decompositions, seasonal adjustment, and the modelling of the serial correlation induced by survey sample design. The issues involved in testing in linear UC models are the theme of part III, which considers tests concerned with whether or not certain variance parameters are zero, with special reference to stationarity tests. Finally, part IV is devoted to the advances concerning classical and Bayesian inference for non linear and non Gaussian state space models, an area that has been evolving very rapidly during the last decade, paralleling the advances in computational inference using stochastic simulation techniques. The book is intended to give a relatively self-contained presentation of the methods and applicative issues. For this purpose, each part comes with an introductory chapter by the editors that provides a unified view of the literature and the many important developments that have occurred in the last years. -


Forecasting, Structural Time Series Models and the Kalman Filter

Forecasting, Structural Time Series Models and the Kalman Filter
Author: Andrew C. Harvey
Publisher: Cambridge University Press
Total Pages: 574
Release: 1990
Genre: Business & Economics
ISBN: 9780521405737

Download Forecasting, Structural Time Series Models and the Kalman Filter Book in PDF, ePub and Kindle

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.