Multiple time series models

By:

Material type: Text

TextSeries: Quantitative applications in the social sciences ; no. 07-148.Publication details: New Delhi Sage 2007Description: xiii,99p. ill. ; 22 cmISBN:

9781412906562

Subject(s):

Time-series analysis -- Mathematical models

DDC classification:

519.55 22 BR-M

LOC classification:

HA30.3 .B73 2007

Online resources:

Table of contents only

Contents:

1. Introduction to multiple time series models -- 2. Basic vector autoregression models -- 3. Examples of VAR analyses -- App. Software for multiple time series models.

Review: "Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data, including simultaneous equations, ARIMA, error correction models, and vector autoregression. Authors Patrick T. Brandt and John T. Williams focus on vector autoregression (VAR) models as a generalization of these other approaches and discuss specification, estimation, and inference using these models." "This text is intended for advanced undergraduate and graduate courses on time series analysis, quantitative research methods, or more advanced statistics, especially in the departments of Sociology, Psychology, Political Science, and Economics. It is also an excellent resource for researchers in the social sciences who are conducting time series analysis or econometric studies."--BOOK JACKET.

Item type:

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Holdings
Item type	Home library	Collection	Call number	Status	Date due	Barcode
Print	OPJGU Sonepat- Campus Central Library	Special Collection - Indiana University	519.55 BR-M (Browse shelf(Opens below))	Available		011447

Includes bibliographical references (p. 92-95) and index.

1. Introduction to multiple time series models -- 2. Basic vector autoregression models -- 3. Examples of VAR analyses -- App. Software for multiple time series models.

"Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data, including simultaneous equations, ARIMA, error correction models, and vector autoregression. Authors Patrick T. Brandt and John T. Williams focus on vector autoregression (VAR) models as a generalization of these other approaches and discuss specification, estimation, and inference using these models." "This text is intended for advanced undergraduate and graduate courses on time series analysis, quantitative research methods, or more advanced statistics, especially in the departments of Sociology, Psychology, Political Science, and Economics. It is also an excellent resource for researchers in the social sciences who are conducting time series analysis or econometric studies."--BOOK JACKET.

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