This new edition of A.C. Harvey's clearly written, upper-level text has been revised and several sections have been completely rewritten. There is new material on a number of topics, including unit roots, ARCH, and cointegration. The Econometric Analysis of Time Series focuses on the statistical aspects of model building, with an emphasis on providing an understanding of the main ideas and concepts in econometrics rather than presenting a series of rigorous proofs. It explores the way in which recent advances in time series analysis have affected the development of a theory of dynamic econometrics, sets out an integrated approach to the problems of estimation and testing based on the method of maximum likelihood, and presents a coherent strategy for model selection. A.C. Harvey is Professor of Econometrics at the London School of Economics.

The Econometric Analysis of Time Series - 2nd Edition (London School of Economics Handbooks in Economics) (Lse Handbooks in Economics)

Business & Money

Harvey, Andrew C.

Economics

Mit Pr

companion volume to Andrew Harvey's highly successful Econometric Analysis of Time Series, focusing on the estimation, testing, and specification of both univariate and multivariate time series models.
Time Series Models is a companion volume to Andrew Harvey's highly successful Econometric Analysis of Time Series. It takes students to another level from the first book, focusing on the estimation, testing, and specification of both univariate and multivariate time series models. The emphasis is on understanding how time series are analyzed and models constructed. Familiarity with calculus, linear algebra, and statistical interference is assumed.Although Time Series Models pairs well with Harvey's earlier text, it is self-contained. For the second edition, the author has added new sections on nonlinear models, unit roots, structural time series models, intervention analysis, and cointegration. He has addressed new developments, rearranged some material, and changed the emphasis in certain areas.

Time Series Models: 2nd Edition

Forecasting, Structural Time Series Models and the Kalman Filter

This book provides a synthesis of concepts and materials that ordinarily appear separately in time series and econometrics literature, presenting a comprehensive review of both theoretical and applied concepts. Perhaps the most novel feature of the book is its use of Kalman filtering together with econometric and time series methodology. From a technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. This technique was originally developed in control engineering but is becoming increasingly important in economics and operations research. The book is primarily concerned with modeling economic and social time series and with addressing the special problems that the treatment of such series pose.

Cambridge University Press

Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series (Econometric Society Monographs, Series Number 52)

The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling, and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility, such as those arising from data on the range of returns and the time between trades. Furthermore, the more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. As such, there are applications not only to financial data but also to macroeconomic time series and to time series in other disciplines. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling. The practical value of the proposed models is illustrated by fitting them to real data sets.

See the best price to sell, buy, or rent books by Harvey, Andrew C.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Harvey, Andrew C.

See the best price to sell, buy, or rent books by Harvey, Andrew C.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

The Econometric Analysis of Time Series - 2nd Edition (London School of Economics Handbooks in Economics) (Lse Handbooks in Economics)

Time Series Models: 2nd Edition

Forecasting, Structural Time Series Models and the Kalman Filter

Forecasting, Structural Time Series Models and the Kalman Filter

Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series (Econometric Society Monographs, Series Number 52)