- pt. I. The Statistical Analysis of Cointegration. 1. Introduction. 2. The Vector Autoregressive Model. 3. Basic Definitions and Concepts. 4. Cointegration and Representation of Integrated Variables. 5. The I(1) Models and their Interpretation. 6. The Statistical Analysis of I(1) Models. 7. Hypothesis Testing for the Long-Run Coefficients [beta]. 8. Partial Systems and Hypotheses on [alpha]. 9. The I(2) Model and a Test for I(2)
- pt. II. The Probability Analysis of Cointegration. 10. Probability Properties of I(1) Processes. 11. The Asymptotic Distribution of the Test for Cointegrating Rank. 12. Determination of Cointegrating Rank. 13. Asymptotic Properties of the Estimators. 14. The Power Function of the Test for Cointegrating Rank under Local Alternatives. 15. Simulations and Tables
- App. A. Some Mathematical Results
- App. B. Weak Convergence of Probability Measures on R[superscript P] and C[0,1].
This book gives a detailed mathematical and statistical analysis of the cointegrated vector autoregresive model. This model had gained popularity because it can at the same time capture the short-run dynamic properties as well as the long-run equilibrium behaviour of many non-stationary time series. It also allows relevant economic questions to be formulated in a consistent statistical framework. Part I of the book is planned so that it can be used by those who want to apply the methods without going into too much detail about the probability theory. The main emphasis is on the derivation of estimators and test statistics through a consistent use of the Guassian likelihood function. It is shown that many different models can be formulated within the framework of the autoregressive model and the interpretation of these models is discussed in detail. In particular, models involving restrictions on the cointegration vectors and the adjustment coefficients are discussed, as well as the role of the constant and linear drift. In Part II, the asymptotic theory is given the slightly more general framework of stationary linear processes with i.i.d. innovations. Some useful mathematical tools are collected in Appendix A, and a brief summary of weak convergence in given in Appendix B. The book is intended to give a relatively self-contained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. The asymptotic theory requires some familiarity with the theory of weak convergence of stochastic processes. The theory is treated in detail with the purpose of giving the reader a working knowledge of the techniques involved. Many exercises are provided. The theoretical analysis is illustrated with the empirical analysis of two sets of economic data. The theory has been developed in close contract with the application and the methods have been implemented in the computer package CATS in RATS as a result of a rcollaboation with Katarina Juselius and Henrik Hansen.
(source: Nielsen Book Data)
Professor Johansen, a leading statistician working in econometrics, gives a detailed mathematical and statistical analysis of the cointegrated vector autoregressive model, which has been gaining in popularity. The book is a self-contained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. The theoretical analysis is illustrated with the empirical analysis of two sets of economic data. The theory has been developed in close contact with the application and the methods have been implemented in the computer package CATS in RATS. -; This book gives a detailed mathematical and statistical analysis of the cointegrated vector autoregresive model. This model had gained popularity because it can at the same time capture the short-run dynamic properties as well as the long-run equilibrium behaviour of many non-stationary time series. It also allows relevant economic questions to be formulated in a consistent statistical framework.The book is intended to give a relatively self-contained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. The asymptotic theory requires some familiarity with the theory of weak convergence of stochastic processes. The theory is treated in detail with the purpose of giving the reader a working knowledge of the techniques involved.
(source: Nielsen Book Data)