Workbook On Cointegration

Author: Peter Reinhard Hansen
Publisher: Oxford University Press on Demand
ISBN: 9780198776086
Size: 63.34 MB
Format: PDF, ePub
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This workbook consists of exercises taken from Likelihood-Based Inferences in Cointegrated Vector Autoregressive Models by Soren Johansen, together with worked-out solutions. 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.

Unit Roots Cointegration And Structural Change

Author: G. S. Maddala
Publisher: Cambridge University Press
ISBN: 9780521587822
Size: 55.61 MB
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Time series analysis has undergone many changes in recent years with the advent of unit roots and cointegration. Maddala and Kim present a comprehensive review of these important developments and examine structural change. The volume provides an analysis of unit root tests, problems with unit root testing, estimation of cointegration systems, cointegration tests, and econometric estimation with integrated regressors. The authors also present the Bayesian approach to these problems and bootstrap methods for small-sample inference. The chapters on structural change discuss the problems of unit root tests and cointegration under structural change, outliers and robust methods, the Markov-switching model and Harvey's structural time series model. Unit Roots, Cointegration and Structural Change is a major contribution to Themes in Modern Econometrics, of interest both to specialists and graduate and upper-undergraduate students.

Limit Theorems For Nonlinear Cointegrating Regression

Author: Qiying Wang
Publisher: World Scientific
ISBN: 9814675644
Size: 16.34 MB
Format: PDF, ePub, Mobi
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This book provides the limit theorems that can be used in the development of nonlinear cointegrating regression. The topics include weak convergence to a local time process, weak convergence to a mixture of normal distributions and weak convergence to stochastic integrals. This book also investigates estimation and inference theory in nonlinear cointegrating regression. The core context of this book comes from the author and his collaborator's current researches in past years, which is wide enough to cover the knowledge bases in nonlinear cointegrating regression. It may be used as a main reference book for future researchers. Contents:IntroductionConvergence to Local TimeConvergence to a Mixture of Normal DistributionsConvergence to Stochastic IntegralsNonlinear Cointegrating Regression Readership: Graduate students and researchers interested in nonlinear cointegrating regression. Key Features:First of all, this book extends the classical martingale limit theorem. Unlike previous books, for a certain class of martingales, weak convergence to a mixture of normal distributions is established under the convergence in distribution for the conditional varianceThis extension partially removes a barrier in applications of the classical martingale limit theorem to non-parametric estimation and inference with non-stationarityThis extension enhances the effectiveness of the classical martingale limit theorem in the investigation of asymptotics in statistics, econometrics and other fieldsSecondly, this book systemically introduces weak convergence to a local time process and weak convergence to stochastic integrals beyond martingale and semi-martingale structures (This kind of context is new to the field and is particularly useful in the framework of nonlinear cointegration)Finally, this book does not look for the most general theory from the view of probability, but provides enough details for those who are interested in nonlinear cointegrating regressionKeywords:Cointegration;Nonlinear Regression;Martingale;Local Time;Stochastic Integral;A Mixture of Normal Distributions;Nonparametric Estimation;Nonparametric Inference;Limit Theorem;Weak Convergence;Uniform Approximation

A Course In Time Series Analysis

Author: Daniel Peña
Publisher: Wiley-Interscience
ISBN:
Size: 70.74 MB
Format: PDF, ePub, Mobi
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New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâs leading figures in time series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

Likelihood Based Inference In Cointegrated Vector Autoregressive Models

Author: Søren Johansen
Publisher: Oxford University Press on Demand
ISBN: 0198774508
Size: 28.68 MB
Format: PDF, ePub
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This monograph is concerned with the statistical analysis of multivariate systems of non-stationary time series of type I. It applies the concepts of cointegration and common trends in the framework of the Gaussian vector autoregressive model.

Finite Sample Econometrics

Author: Aman Ullah
Publisher: Oxford University Press, USA
ISBN:
Size: 39.79 MB
Format: PDF, Docs
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This book provides a comprehensive and unified treatment of finite sample statistics and econometrics, a field that has evolved in the last five decades. Within this framework, this is the first book which discusses the basic analytical tools of finite sample econometrics, and explores their applications to models covered in a first year graduate course in econometrics, including repression functions, dynamic models, forecasting, simultaneous equations models, panel data models, and censored models. Both linear and nonlinear models, as well as models with normal and non-normal errors, are studied. 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.

Stochastic Volatility

Author: Neil Shephard
Publisher: Oxford University Press, USA
ISBN:
Size: 16.83 MB
Format: PDF, ePub, Docs
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Stochastic volatility is the main concept used in the fields of financial economics and mathematical finance to deal with time-varying volatility in financial markets. This work brings together some of the main papers that have influenced this field, andshows that the development of this subject has been highly multidisciplinary.

The Econometrics Of Macroeconomic Modelling

Author: Gunnar Bårdsen
Publisher: Oxford University Press, USA
ISBN:
Size: 80.81 MB
Format: PDF
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This work describes how the discipline has adapted to changing demands by adopting new insights from economic theory and by taking advantage of the methodological and conceptual advances within time series econometrics.