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Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data

Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data
Author: Anindya Banerjee
Publisher: Oxford University Press
Total Pages: 344
Release: 1993-05-27
Genre: Business & Economics
ISBN: 0191638919

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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.


New Introduction to Multiple Time Series Analysis

New Introduction to Multiple Time Series Analysis
Author: Helmut Lütkepohl
Publisher: Springer Science & Business Media
Total Pages: 792
Release: 2007-07-26
Genre: Business & Economics
ISBN: 9783540262398

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This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.


Causal Inference in Econometrics

Causal Inference in Econometrics
Author: Van-Nam Huynh
Publisher: Springer
Total Pages: 626
Release: 2015-12-28
Genre: Technology & Engineering
ISBN: 3319272845

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This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.


Multivariate Modelling of Non-Stationary Economic Time Series

Multivariate Modelling of Non-Stationary Economic Time Series
Author: John Hunter
Publisher: Springer
Total Pages: 508
Release: 2017-05-08
Genre: Business & Economics
ISBN: 113731303X

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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 Analysis for the Social Sciences

Time Series Analysis for the Social Sciences
Author: Janet M. Box-Steffensmeier
Publisher: Cambridge University Press
Total Pages: 297
Release: 2014-12-22
Genre: Mathematics
ISBN: 0521871166

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This book provides instruction and examples of the core methods in time series econometrics, drawing from several main fields of the social sciences.