Modelling Non Stationary Economic Time Series
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Modelling Non Stationary Economic Time Series
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Author : S. Burke
language : en
Publisher: Springer
Release Date : 2005-06-14
Modelling Non Stationary Economic Time Series written by S. Burke and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-14 with Business & Economics categories.
Co-integration, equilibrium and equilibrium correction are key concepts in modern applications of econometrics to real world problems. This book provides direction and guidance to the now vast literature facing students and graduate economists. Econometric theory is linked to practical issues such as how to identify equilibrium relationships, how to deal with structural breaks associated with regime changes and what to do when variables are of different orders of integration.
Forecasting Non Stationary Economic Time Series
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Author : Michael P. Clements
language : en
Publisher: MIT Press
Release Date : 1999
Forecasting Non Stationary Economic Time Series written by Michael P. Clements and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Business & Economics categories.
This text on economic forecasting asks why some practices seem to work empirically despite a lack of formal support from theory. After reviewing the conventional approach to forecasting, it looks at the implications for causal modelling, presents forecast errors and delineates sources of failure.
Multivariate Modelling Of Non Stationary Economic Time Series
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Author : John Hunter
language : en
Publisher: Springer
Release Date : 2017-05-08
Multivariate Modelling Of Non Stationary Economic Time Series written by John Hunter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-08 with Business & Economics categories.
This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction, volatility and the impact of different orders of integration. Models with expectations are considered along with alternate methods such as Singular Spectrum Analysis (SSA), the Kalman Filter and Structural Time Series, all in relation to cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide direction and guidance to the now vast literature facing students and graduate economists.
Time Series Models For Business And Economic Forecasting
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Author : Philip Hans Franses
language : en
Publisher: Cambridge University Press
Release Date : 1998-10-15
Time Series Models For Business And Economic Forecasting written by Philip Hans Franses and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-10-15 with Business & Economics categories.
An introduction to time series models for business and economic forecasting.
Introduction To Modern Time Series Analysis
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Author : Gebhard Kirchgässner
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-10-09
Introduction To Modern Time Series Analysis written by Gebhard Kirchgässner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-09 with Business & Economics categories.
This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.
Modelling Nonlinear Economic Time Series
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Author : Timo Teräsvirta
language : en
Publisher: OUP Oxford
Release Date : 2010-12-16
Modelling Nonlinear Economic Time Series written by Timo Teräsvirta and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-16 with Business & Economics categories.
This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series models. Importantly, it shows the reader how to apply these models in practice. For thispurpose, the building of various nonlinear models with its three stages of model building: specification, estimation and evaluation, is discussed in detail and is illustrated by several examples involving both economic and non-economic data. Since estimation of nonlinear time series models is carried outusing numerical algorithms, the book contains a chapter on estimating parametric nonlinear models and another on estimating nonparametric ones.Forecasting is a major reason for building time series models, linear or nonlinear. The book contains a discussion on forecasting with nonlinear models, both parametric and nonparametric, and considers numerical techniques necessary for computing multi-period forecasts from them. The main focus of the book is on models of the conditional mean, but models of the conditional variance, mainly those of autoregressive conditional heteroskedasticity, receive attention as well. A separate chapter isdevoted to state space models. As a whole, the book is an indispensable tool for researchers interested in nonlinear time series and is also suitable for teaching courses in econometrics and time series analysis.
Forecasting Economic Time Series
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Author : Michael Clements
language : en
Publisher: Cambridge University Press
Release Date : 1998-10-08
Forecasting Economic Time Series written by Michael Clements and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-10-08 with Business & Economics categories.
This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.
Modelling Our Changing World
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Author : Jennifer L. Castle
language : en
Publisher: Springer Nature
Release Date : 2019-08-30
Modelling Our Changing World written by Jennifer L. Castle and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-30 with Business & Economics categories.
This open access book focuses on the concepts, tools and techniques needed to successfully model ever-changing time-series data. It emphasizes the need for general models to account for the complexities of the modern world and how these can be applied to a range of issues facing Earth, from modelling volcanic eruptions, carbon dioxide emissions and global temperatures, to modelling unemployment rates, wage inflation and population growth. Except where otherwise noted, this book is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0.
Co Integration Error Correction And The Econometric Analysis Of Non Stationary Data
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Author : Anindya Banerjee
language : en
Publisher: Oxford University Press
Release Date : 1993-05-27
Co Integration Error Correction And The Econometric Analysis Of Non Stationary Data written by Anindya Banerjee and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-05-27 with Business & Economics categories.
This book provides a wide-ranging account of the literature on co-integration and the modelling of integrated processes (those which accumulate the effects of past shocks). Data series which display integrated behaviour are common in economics, although techniques appropriate to analysing such data are of recent origin and there are few existing expositions of the literature. This book focuses on the exploration of relationships among integrated data series and the exploitation of these relationships in dynamic econometric modelling. The concepts of co-integration and error-correction models are fundamental components of the modelling strategy. This area of time-series econometrics has grown in importance over the past decade and is of interest to econometric theorists and applied econometricians alike. By explaining the important concepts informally, but also presenting them formally, the book bridges the gap between purely descriptive and purely theoretical accounts of the literature. The asymptotic theory of integrated processes is described and the tools provided by this theory are used to develop the distributions of estimators and test statistics. Practical modelling advice, and the use of techniques for systems estimation, are also emphasized. A knowledge of econometrics, statistics, and matrix algebra at the level of a final-year undergraduate or first-year undergraduate course in econometrics is sufficient for most of the book. Other mathematical tools are described as they occur.
Periodic Time Series Models
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Author : Philip Hans Franses
language : en
Publisher: OUP Oxford
Release Date : 2004-03-25
Periodic Time Series Models written by Philip Hans Franses and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-03-25 with Business & Economics categories.
This book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness for out-of-sample forecasting. Providing an up-to-date survey of the recent developments in periodic time series, the book presents a large number of empirical results. The first part of the book deals with model selection, diagnostic checking and forecasting of univariate periodic autoregressive models. Tests for periodic integration, are discussed, and an extensive discussion of the role of deterministic regressors in testing for periodic integration and in forecasting is provided. The second part discusses multivariate periodic autoregressive models. It provides an overview of periodic cointegration models, as these are the most relevant. This overview contains single-equation type tests and a full-system approach based on generalized method of moments. All methods are illustrated with extensive examples, and the book will be of interest to advanced graduate students and researchers in econometrics, as well as practitioners looking for an understanding of how to approach seasonal data.