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Springer New Introduction to Multiple Time Series Analysis

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Description

Master the complexities of multivariate data with this comprehensive reference work and graduate-level textbook from Springer. Designed for students and professionals in econometrics, this book provides a deep dive into the models and methods required for analyzing and forecasting multiple time series. Readers will gain a thorough understanding of essential frameworks, including vector autoregressive, cointegrated, and vector autoregressive moving average models. The text also covers multivariate ARCH, periodic processes, dynamic simultaneous equations, and state space models. By exploring least squares, maximum likelihood, and Bayesian methods for model estimation, you can build a strong foundation for advanced statistical work. Beyond model building, this resource guides you through the practical aspects of model selection and specification. It introduces a wide range of tests and criteria for model checking, ensuring you have the tools to validate your results. Whether you are studying economics or applying econometric methods to business and finance, this text serves as a vital guide for navigating modern time series analysis.

Key Features

Comprehensive coverage of advanced models including vector autoregressive, cointegrated, and vector autoregressive moving average processes.

In-depth exploration of multivariate ARCH, periodic processes, and state space models for complex data analysis.

Detailed instruction on estimation techniques using least squares, maximum likelihood, and Bayesian methods.

Practical guidance on model selection and specification procedures to improve analytical accuracy.

A wide range of tests and criteria for model checking to ensure robust forecasting and results.

Designed as both a graduate-level textbook and a professional reference work for econometric studies.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
05 July 2005
Listed Since
08 January 2007

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No barcode data available

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