Multivariate Time Series Analysis With R and Financial Applications.

Detalles Bibliográficos
Autor principal: Tsay, Ruey S.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2013.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Multivariate Time Series Analysis: With R and Financial Applications
  • Copyright
  • Contents
  • Preface
  • Acknowledgements
  • 1 Multivariate Linear Time Series
  • 1.1 Introduction
  • 1.2 Some Basic Concepts
  • 1.2.1 Stationarity
  • 1.2.2 Linearity
  • 1.2.3 Invertibility
  • 1.3 Cross-Covariance and Correlation Matrices
  • 1.4 Sample CCM
  • 1.5 Testing Zero Cross-Correlations
  • 1.6 Forecasting
  • 1.7 Model Representations
  • 1.8 Outline of the Book
  • 1.9 Software
  • Exercises
  • 2 Stationary Vector Autoregressive Time Series
  • 2.1 Introduction
  • 2.2 VAR(1) Models
  • 2.2.1 Model Structure and Granger Causality
  • 2.2.2 Relation to Transfer Function Model
  • 2.2.3 Stationarity Condition
  • 2.2.4 Invertibility
  • 2.2.5 Moment Equations
  • 2.2.6 Implied Models for the Components
  • 2.2.7 Moving-Average Representation
  • 2.3 VAR(2) Models
  • 2.3.1 Stationarity Condition
  • 2.3.2 Moment Equations
  • 2.3.3 Implied Marginal Component Models
  • 2.3.4 Moving-Average Representation
  • 2.4 VAR(p) Models
  • 2.4.1 A VAR(1) Representation
  • 2.4.2 Stationarity Condition
  • 2.4.3 Moment Equations
  • 2.4.4 Implied Component Models
  • 2.4.5 Moving-Average Representation
  • 2.5 Estimation
  • 2.5.1 Least-Squares Methods
  • 2.5.2 Maximum Likelihood Estimate
  • 2.5.3 Limiting Properties of LS Estimate
  • 2.5.4 Bayesian Estimation
  • 2.6 Order Selection
  • 2.6.1 Sequential Likelihood Ratio Tests
  • 2.6.2 Information Criteria
  • 2.7 Model Checking
  • 2.7.1 Residual Cross-Correlations
  • 2.7.2 Multivariate Portmanteau Statistics
  • 2.7.3 Model Simplification
  • 2.8 Linear Constraints
  • 2.9 Forecasting
  • 2.9.1 Forecasts of a Given Model
  • 2.9.2 Forecasts of an Estimated Model
  • 2.10 Impulse Response Functions
  • 2.10.1 Orthogonal Innovations
  • 2.11 Forecast ErrorVariance Decomposition
  • 2.12 Proofs
  • Exercises
  • References
  • 3 Vector Autoregressive Moving-Average Time Series
  • 3.1 Vector MA Models
  • 3.1.1 VMA(1) Model
  • 3.1.2 Properties of VMA(q) Models
  • 3.2 Specifying VMA Order
  • 3.3 Estimation of VMA Models
  • 3.3.1 Conditional Likelihood Estimation
  • 3.3.2 Exact Likelihood Estimation
  • 3.3.3 Initial Parameter Estimation
  • 3.4 Forecasting of VMA Models
  • 3.5 VARMA Models
  • 3.5.1 Identifiability
  • 3.5.2 VARMA(1,1) Models
  • 3.5.3 Some Properties of VARMA Models
  • 3.6 Implications of VARMA Models
  • 3.6.1 Granger Causality
  • 3.6.2 Impulse Response Functions
  • 3.7 Linear Transforms of VARMA Processes
  • 3.8 Temporal Aggregation of VARMA Processes
  • 3.9 Likelihood Function of a VARMA Model
  • 3.9.1 Conditional Likelihood Function
  • 3.9.2 Exact Likelihood Function
  • 3.9.3 Interpreting the Likelihood Function
  • 3.9.4 Computation of Likelihood Function
  • 3.10 Innovations Approach to Exact Likelihood Function
  • 3.10.1 Block Cholesky Decomposition
  • 3.11 Asymptotic Distribution of Maximum Likelihood Estimates
  • 3.11.1 Linear Parameter Constraints

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