New Introduction to Multiple Time Series Analysis

Helmut Lütkepohl

ISBN 10: 3540262393 / ISBN 13: 9783540262398

Verlag: Springer, Berlin, Springer Berlin Heidelberg, Springer Apr 2006, 2006

Beschreibung

Neuware - This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated,vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic. Bestandsnummer des Verkäufers 9783540262398

Bibliografische Details

Titel: New Introduction to Multiple Time Series ...

Verlag: Springer, Berlin, Springer Berlin Heidelberg, Springer Apr 2006

Erscheinungsdatum: 2006

Einband: Taschenbuch

Zustand: Neu

Über diesen Titel

Inhaltsangabe:

Book by Ltkepohl Helmut

Reseña del editor:

This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.