New Introduction to Multiple Time Series Analysis
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.
1129012790
New Introduction to Multiple Time Series Analysis
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.
139.99 In Stock
New Introduction to Multiple Time Series Analysis

New Introduction to Multiple Time Series Analysis

by Helmut Lïtkepohl
New Introduction to Multiple Time Series Analysis

New Introduction to Multiple Time Series Analysis

by Helmut Lïtkepohl

Paperback(2005)

$139.99 
  • SHIP THIS ITEM
    In stock. Ships in 6-10 days.
    Not Eligible for Free Shipping
  • PICK UP IN STORE

    Your local store may have stock of this item.

Related collections and offers


Overview

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.

Product Details

ISBN-13: 9783540262398
Publisher: Springer Berlin Heidelberg
Publication date: 03/14/2006
Edition description: 2005
Pages: 764
Product dimensions: 6.10(w) x 9.25(h) x 0.06(d)

Table of Contents

Finite Order Vector Autoregressive Processes.- Stable Vector Autoregressive Processes.- Estimation of Vector Autoregressive Processes.- VAR Order Selection and Checking the Model Adequacy.- VAR Processes with Parameter Constraints.- Cointegrated Processes.- Vector Error Correction Models.- Estimation of Vector Error Correction Models.- Specification of VECMs.- Structural and Conditional Models.- Structural VARs and VECMs.- Systems of Dynamic Simultaneous Equations.- Infinite Order Vector Autoregressive Processes.- Vector Autoregressive Moving Average Processes.- Estimation of VARMA Models.- Specification and Checking the Adequacy of VARMA Models.- Cointegrated VARMA Processes.- Fitting Finite Order VAR Models to Infinite Order Processes.- Time Series Topics.- Multivariate ARCH and GARCH Models.- Periodic VAR Processes and Intervention Models.- State Space Models.
From the B&N Reads Blog

Customer Reviews