Time Series, Unit Roots, and Cointegration
This book addresses the need for a high-level analysis of unit roots and cointegration. "Time Series, Unit Roots, and Cointegration" integrates the theory of stationary sequences and issues arising in the estimation of their parameters, distributed lags, spectral density function, and cointegration. The book also includes topics that are important for understanding recent developments in the estimation and testing of cointegrated nonstationary sequences, such as Brownian motion, stochastic integration, and central limit theorems. It explores an important topic in time-series econometrics. It addresses the need for a high-level analysis of unit roots and cointegration. It is written by an excellent expositor.
- Hardback | 524 pages
- 182.9 x 266.2 x 27.2mm | 925.34g
- 02 Dec 1997
- Emerald Publishing Limited
- Academic Press Inc
- Bingley, United Kingdom
"Dhrymes' new book deals exclusively and rigorously with an extremely important topic in time-series econometrics. Dhrymes is terribly good at proving theorems; this unified and careful treatment will be useful for teachers, students, and practitioners of advanced econometrics. It will serve as supplementary reading in time-series courses, as a text for a very advanced special topics course, and as a standard reference in the field. If you want to cite a theorem and its proof, here it is." --MARC NERLOVE, Department of Agricultural and Resource Economics, University of Maryland, College Park
Table of contents
Stochastic Sequences. Prediction and Estimation. Unit Roots; I(1) Regressors. Cointegration I. Cointegration II. Cointegration III. Brownian Motion. Stochastic Integration. Central Limit Theorems; Invariance. Frequently Used Symbols. Graphs of Sequences of Various Types. Bibliography. Index.
About Phoebus J. Dhrymes
Professor Dhrymes is a Professor of Economics at Columbia University and a Fellow in the Econometric Society and the American Statistical Association. He is a recipient of Guggenheim, Ford Foundation, and NSF fellowships, and publishes widely on subjects in econometrics.