Introduction to Multiple Time Series Analysis

Lu?tkepohl, Helmut

ISBN 10: 3540531947 ISBN 13: 9783540531944

Editorial: Springer-Verlag, 1991

Idioma: Ingl�s

Condici�n: Nuevo Encuadernaci�n de tapa blanda

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Descripci�n

pp. 545.

N� de ref. del art�culo 26357819535

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Detalles bibliogr�ficos

T�tulo: Introduction to Multiple Time Series Analysis
Editor: Springer-Verlag
Fecha de publicaci�n: 1991
Idioma: Ingl�s
ISBN 10: 3540531947
ISBN 13: 9783540531944
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

This graduate-level textbook deals with analyzing and forecasting multiple time series. It considers a wide range of multiple time series models and methods. The models include vector autoregressive, vector autoregressive moving average, cointegrated and periodic processes as well as state space and dynamic simultaneous equations models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models. Different procedures for model selection or specification are treated and a range of tests and criteria for evaluating the adequacy of a chosen model are introduced. The choice of point and interval forecasts as well as innovation accounting are presented as tools for structural analysis within the multiple time series context.

Rese�a del editor

This graduate-level textbook deals with analyzing and forecasting multiple time series. It considers a wide range of multiple time series models and methods. The models include vector autoregressive, vector autoregressive moving average, cointegrated and periodic processes as well as state space and dynamic simultaneous equations models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models. Different procedures for model selection or specification are treated and a range of tests and criteria for evaluating the adequacy of a chosen model are introduced. The choice of point and interval forecasts as well as innovation accounting are presented as tools for structural analysis within the multiple time series context.

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