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5 edition of Co-integration, Error Correction and the Econometric Analysis of Non-stationary Data found in the catalog.

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

Anindya Banerjee

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

by Anindya Banerjee

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  • 6 Currently reading

Published by Oxf. U. P. .
Written in English


Edition Notes

StatementAnindya Banerjee,etc..
SeriesAdvanced Texts in Econometrics
The Physical Object
Pagination(256)p. :
Number of Pages256
ID Numbers
Open LibraryOL21172753M
ISBN 100198288107

Introduction. If two or more series are individually integrated (in the time series sense) but some linear combination of them has a lower order of integration, then the series are said to be cointegrated.A common example is where the individual series are first-order integrated (()) but some (cointegrating) vector of coefficients exists to form a stationary linear combination of them. Presents the important properties of integrated variables and sets out some of the preliminary asymptotic theories essential for the consideration of such processes. It explores the concepts of unit roots, non‐stationarity, orders of integration, and near integration, and demonstrates the use of the theory in understanding the behaviour of least‐squares estimators in spurious regressions.

The chapter demonstrates the special problems with inference that arise, for example, in orthogonality tests for rational expectations, from the presence of integrated variables. Using results from Sims, Stock and Watson (), we show how a proper consideration of the deterministics, the orders of integration of the variables, and dynamic specifications that take into account any co. This monograph deals with spatially dependent nonstationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis. After charting key concepts in both time series and spatial econometrics, the book discusses how the spatial connectivity matrix can be estimated using.

Advanced: Freeman, John. “Granger Causality and the Time Series Analysis of Political Relationships.” American Journal of Political Science. Very Advanced: Banerjee, Anindya, Juan Dolado, John W. Galbraith and David F Hendry. Co-integration, Error-Correction, and the Econometric Analysis of Non -Stationary Data. Banerjee et al. (): “ Error-Correction Mechanism Tests for Cointegration in a Single- Equation Framework”, Journal of Time Series Analysis, 19, 3, Bewley R. and (): “On the size and Power of system tests for.


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Co-integration, Error Correction and the Econometric Analysis of Non-stationary Data by Anindya Banerjee Download PDF EPUB FB2

This book considers the econometric analysis of both stationary and non‐stationary processes, which may be linked by equilibrium relationships. It provides a wide‐ranging account of the main tools, techniques, models, concepts, and distributions involved in the modelling of integrated processes (i.e.

those that accumulate the effects of past shocks). 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. Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data (Advanced Texts in Econometrics) 1st Edition by Anindya Banerjee (Author), Juan Dolado (Contributor), Juan W.

Galbraith (Contributor), out of 5 stars Error Correction and the Econometric Analysis of Non-stationary Data book ratings ISBN Cited by: 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 book explores relationships among integrated data series and their use in dynamic econometric modelling. The concepts of cointegration and error-correction models are fundamental components of the modelling strategy.

Co-integration, Error Correction, and the Econometric Analysis of Non-stationary Data Anindya Banerjee, Juan Dolado, J. Galbraith, David Hendry Oxford University Press, - Business & 5/5(1). Co-integration, Error Correction and the Econometric Analysis of Non-stationary Data (Advanced Texts in Econometrics) by Banerjee Anindya Dolado Juan Galbraith W.

Hendry David F. () Hardcover Hardcover – January 1, out of 5 stars 3 ratings See all formats and editions/5(3). Co-integration, error correction, and the econometric analysis of non-stationary data. [Anindya Banerjee;] -- This book is wide-ranging in its account of literature on cointegration and the modelling of integrated processes (those which accumulate the effects of past shocks).

Data series which display. •Modern econometric analysis emphasise the importance of unit root testing in conducting empirical econometric work. •Granger and Newbold () non-stationary data yield misleading or spurious regression results i.e. regressions that do not make sense e.g.

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.

Banerjee / Dolado / Galbraith, Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data,Buch, Bücher schnell. Author(s): Banerjee, Anindya & Dolado, Juan J.

& Galbraith, John W. & Hendry, David. Abstract: 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. Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data (Advanced Texts in Econometrics) Anindya Banerjee, Juan Dolado, Juan W.

Galbraith, David F. Hendry Published by Oxford University Press, USA (). Econometric Analysis of Cointegration Presented by the Department of Economics, University of Pretoria Course content Overview of residual-based co-integration • Data generating processes • Stationary vs.

non-stationary time series • Co-integration in single equations (Enge-Granger) • Error-correction models (ECM). CO-INTEGRATION AND ERROR CORRECTION (b) If x, -(1) with xo = 0, then (i) variance x, goes to infinity as t goes to infinity; (ii) an innovation has a permanent effect on the value of x, as x, is the.

The key concept of co‐integration of integrated time series is defined, and several examples are presented. An important theorem due to Granger on alternative representations of a system of co‐integrated variables is stated and its proof is sketched.

The chapter then discusses the Engle–Granger two‐step procedure for estimating the parameters characterizing the co‐integrating. It is shown that this model encompasses a wide series of simpler models frequently used in the analysis of space-time data as well as models that better fit the data and have never been used before.

A framework is developed to determine which model is the most likely candidate to study space-time data. Co‐integration in systems of equations is analysed. Linear co‐integrated systems are expressed in error‐correction form and maximum likelihood estimation and inference for co‐integrating vectors are discussed, focusing on the approach proposed by Johansen ().

Methods of finding the co‐integrating rank are considered and circumstances in which dynamic single‐equation methods.

This book focuses on the exploration of relationships among integrated data series and the exploitation of these relationships in dynamic econometric modelling. Examines methods of testing for co‐integration in single equations via static regressions, and provides simulation estimates of the percentiles of the distributions of statistics used in these tests.

The finite‐sample biases of the estimates of the co‐integrating vectors and powers of the tests based on static regressions are discussed within the framework of extensive Monte Carlo. Methods of testing for a unit root in an observed series are described in this chapter.

Both parametric regression tests and non‐parametric adjustments to these test statistics are considered, and tables of critical values for commonly used tests are given. The chapter also uses functionals of Wiener processes to describe the asymptotic distributions of important test statistics.Hendry DF, Ericsson NR () An econometric analysis of the uk money demand in ‘monetary trends in the united states and the United Kingdom’ by Friedman M.

A Co-Integration Analysis of the Interdependencies between Crude Oil and Distillate Fuel Prices Jane Aduda, Patrick Weke, Philip Ngare DOI: /jmf Downloads Views Citations.