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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 is considered and impulse response analysis, dynamic multipliers as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. This 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 this book. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their task. It enables the reader to perform his or her analyses in a gap to the difficult technical literature on the topic.
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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 is considered and impulse response analysis, dynamic multipliers as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. This 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 this book. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their task. It enables the reader to perform his or her analyses in a gap to the difficult technical literature on the topic.
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Introduction to Multiple Time Series Analysis.
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Springer Verlag, Berlin, 1993
ISBN 10: 3540569405
ISBN 13: 9783540569404
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Second Edition,. XXI, 545 Pages with 34 Figures, 3540569405 Sprache: Englisch Gewicht in Gramm: 900 Groß 8°, Original-Karton (Softcover), sehr gutes und innen sauberes Exemplar, Nº de ref. del artículo: 80172
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Introduction to Multiple Time Series Analysis
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Condición: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present. Nº de ref. del artículo: M03540569405-G
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Introduction to Multiple Time Series Analysis.
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Springer; Bertlin,, 1993
ISBN 10: 3540569405
ISBN 13: 9783540569404
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second edition. 545 Seiten Hinterer Deckel mit leichter Kratzspur, sonst sehr gut erhalten. Sprache: Englisch Gewicht in Gramm: 891 17,0 x 3,3 x 24,2 cm, OKarton, Broschiert. Nº de ref. del artículo: 66833
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Introduction to Multiple Time Series Analysis
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Springer Berlin Heidelberg Aug 1993, 1993
ISBN 10: 3540569405
ISBN 13: 9783540569404
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 is considered and impulse response analysis, dynamic multipliers as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. This 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 this book. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their task. It enables the reader to perform his or her analyses in a gap to the difficult technical literature on the topic. 568 pp. Englisch. Nº de ref. del artículo: 9783540569404
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Introduction to Multiple Time Series Analysis
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. In. Nº de ref. del artículo: ria9783540569404_new
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Introduction to Multiple Time Series Analysis
Publicado por
Springer Berlin Heidelberg, 1993
ISBN 10: 3540569405
ISBN 13: 9783540569404
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 is considered and impulse response analysis, dynamic multipliers as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. This 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 this book. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their task. It enables the reader to perform his or her analyses in a gap to the difficult technical literature on the topic. Nº de ref. del artículo: 9783540569404
Cantidad disponible: 1 disponibles
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Introduction to Multiple Time Series Analysis
Librería: California Books, Miami, FL, Estados Unidos de America
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Condición: New. Nº de ref. del artículo: I-9783540569404
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Introduction to Multiple Time Series Analysis
Publicado por
Springer Berlin Heidelberg, 1993
ISBN 10: 3540569405
ISBN 13: 9783540569404
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Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This graduate level textbook deals with analyzing and forecasting multiple time series. The models discussed include vector autoregressive, vector autoregressive moving average, cointegrated, and periodic processes as well as state space and dynamic simulta. Nº de ref. del artículo: 4894125
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Introduction to Multiple Time Series Analysis
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Introduction To Multiple Time Series Analysis
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Condición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Nº de ref. del artículo: ABNR-87345
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