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

by ; ; ;
Format: Hardcover
Pub. Date: 1993-07-29
Publisher(s): Oxford University Press
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Summary

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 integrated behavior are common in economics, although techniques appropriate to analyzing such data are relatively new, with few existing expositions of the literature. 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. This area of time series econometrics has grown in importance over the past decade and is of interest to both econometric theorists and applied econometricians. By explaining the important concepts informally and presenting them formally, the book bridges the gap between purely descriptive and purely theoretical accounts of the literature. The work describes the asymptotic theory of integrated processes and uses the tools provided by this theory to develop the distributions of estimators and test statistics. It emphasizes practical modelling advice and the use of techniques for systems estimation. 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.

Table of Contents

Notational Conventions, Symbols, and Abbrreviations xi
Introduction and Overview
1(45)
Equilibrium relationships and the long run
2(2)
Stationarity and equilibrium relationships
4(1)
Equilibrium and the specification of dynamic models
5(3)
Estimation of long-run relationships and testing for orders of integration and co-integration
8(2)
Preliminary concepts and definitions
10(18)
Data representation and transformations
28(4)
Examples: typical ARMA processes
32(8)
Empirical time series: money, prices, output, and interest rates
40(2)
Outline of later chapters
42(4)
Appendix
43(3)
Linear Transformations, Error Correction, and the Long Run in Dynamic Regression
46(23)
Transformations of a simple model
48(2)
The error-correction model
50(2)
An example
52(1)
Bardsen and Bewley transformations
53(2)
Equivalence of estimates from different transformations
55(5)
Homogeneity and the ECM as a linear transformation of the ADL
60(1)
Variances of estimates of long-run multipliers
61(3)
Expectational variables and the interpretation of long-run solutions
64(5)
Properties of Integrated Processes
69(30)
Spurious regression
70(11)
Trends and random walks
81(3)
Some statistical features of integrated processes
84(2)
Asymptotic theory for integrated processes
86(5)
Using Wiener distribution theory
91(4)
Near-integrated processes
95(4)
Testing for a Unit Root
99(37)
Similar tests and exogenous regressors in the DGP
104(2)
General dynamic models for the process of interest
106(2)
Non-parametric tests for a unit root
108(5)
Tests on more than one parameter
113(6)
Further extensions
119(4)
Asymptotic distributions of test statistics
123(13)
Co-integration
136(26)
An example
137(3)
Polynomial matrices
140(5)
Integration and co-integration: formal definitions and theorems
145(8)
Significance of alternative representations
153(1)
Alternative representations of co-integrated variables: two examples
153(4)
Engle---Granger two---step procedure
157(5)
Regression with Integrated Variables
162(42)
Unbalanced regressions and orthogonality tests
164(4)
Dynamic regressions
168(24)
Functional forms and transformations
192(12)
Appendix: Vector Brownian Motion
200(4)
Co-integration in Individual Equations
204(51)
Estimating a single co-integrating vector
205(1)
Tests for co-integration in a single equation
206(5)
Response surfaces for critical values
211(3)
Finite-sample biases in OLS estimates
214(16)
Powers of single-equation co-integration tests
230(6)
An empirical illustration
236(3)
Fully modified estimation
239(1)
A fully modified least-squares estimator
240(2)
Dynamic specification
242(2)
Examples
244(11)
Appendix: Covariance Matrices
252(3)
Co-integration in Systems of Equations
255(44)
Co-integration and error correction
257(4)
Estimating co-integrating vectors in systems
261(5)
Inference about the co-integration space
266(2)
An empirical illustration
268(3)
Extensions
271(21)
A second example of the Johansen maximum likelihood approach
292(1)
Asymptotic distributions of estimators of co-integrating vectors in I(1) systems
293(6)
Conclusion
299(12)
Summary
299(1)
The invariance of co-integrating vectors
300(1)
Invariance of co-integration under seasonal adjustment
301(2)
Structured time-series models and co-integration
303(1)
Recent research on integration and co-integration
304(3)
Reinterpreting econometrics time-series problems
307(4)
References 311(10)
Acknowledgements for Quoted Extracts 321(2)
Author Index 323(2)
Subject Index 325

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