A course in time series analysis /
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| Imprint: | New York : John Wiley, c2001. |
|---|---|
| Description: | xvii, 460 p. : ill. ; 25 cm. |
| Language: | English |
| Series: | Wiley series in probability and statistics : probability and statistics section. Wiley series in probability and statistics. Probability and statistics. |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/4375623 |
Table of Contents:
- Preface
- About ECAS
- Contributors
- 1.. Introduction
- 1.1.. Examples of time series problems
- 1.1.1.. Stationary series
- 1.1.2.. Nonstationary series
- 1.1.3.. Seasonal series
- 1.1.4.. Level shifts and outliers in time series
- 1.1.5.. Variance changes
- 1.1.6.. Asymmetric time series
- 1.1.7.. Unidirectional-feedback relation between series
- 1.1.8.. Comovement and cointegration
- 1.2.. Overview of the book
- 1.3.. Further reading
- Part I. Basic Concepts in Univariate Time Series
- 2.. Univariate Time Series: Autocorrelation, Linear Prediction, Spectrum, and State-Space Model
- 2.1.. Linear time series models
- 2.2.. The autocorrelation function
- 2.3.. Lagged prediction and the partial autocorrelation function
- 2.4.. Transformations to stationarity
- 2.5.. Cycles and the periodogram
- 2.6.. The spectrum
- 2.7.. Further interpretation of time series acf, pacf, and spectrum
- 2.8.. State-space models and the Kalman Filter
- 3.. Univariate Autoregressive Moving-Average Models
- 3.1.. Introduction
- 3.1.1.. Univariate ARMA models
- 3.1.2.. Outline of the chapter
- 3.2.. Some basic properties of univariate ARMA models
- 3.2.1.. The [phi] and [pi] weights
- 3.2.2.. Stationarity condition and autocovariance structure of z[subscript t]
- 3.2.3.. The autocorrelation function
- 3.2.4.. The partial autocorrelation function
- 3.2.5.. The extended autocorrelation function
- 3.3.. Model specification strategy
- 3.3.1.. Tentative specification
- 3.3.2.. Tentative model specification via SEACF
- 3.4.. Examples
- 4.. Model Fitting and Checking, and the Kalman Filter
- 4.1.. Prediction error and the estimation criterion
- 4.2.. The likelihood of ARMA models
- 4.3.. Likelihoods calculated using orthogonal errors
- 4.4.. Properties of estimates and problems in estimation
- 4.5.. Checking the fitted model
- 4.6.. Estimation by fitting to the sample spectrum
- 4.7.. Estimation of structural models by the Kalman filter
- 5.. Prediction and Model Selection
- 5.1.. Introduction
- 5.2.. Properties of minimum mean-square error prediction
- 5.2.1.. Prediction by the conditional expectation
- 5.2.2.. Linear predictions
- 5.3.. The computation of ARIMA forecasts
- 5.4.. Interpreting the forecasts from ARIMA models
- 5.4.1.. Nonseasonal models
- 5.4.2.. Seasonal models
- 5.5.. Prediction confidence intervals
- 5.5.1.. Known parameter values
- 5.5.2.. Unknown parameter values
- 5.6.. Forecast updating
- 5.6.1.. Computing updated forecasts
- 5.6.2.. Testing model stability
- 5.7.. The combination of forecasts
- 5.8.. Model selection criteria
- 5.8.1.. The FPE and AIC criteria
- 5.8.2.. The Schwarz criterion
- 5.9.. Conclusions
- 6.. Outliers, Influential Observations, and Missing Data
- 6.1.. Introduction
- 6.2.. Types of outliers in time series
- 6.2.1.. Additive outliers
- 6.2.2.. Innovative outliers
- 6.2.3.. Level shifts
- 6.2.4.. Outliers and intervention analysis
- 6.3.. Procedures for outlier identification and estimation
- 6.3.1.. Estimation of outlier effects
- 6.3.2.. Testing for outliers
- 6.4.. Influential observations
- 6.4.1.. Influence on time series
- 6.4.2.. Influential observations and outliers
- 6.5.. Multiple outliers
- 6.5.1.. Masking effects
- 6.5.2.. Procedures for multiple outlier identification
- 6.6.. Missing-value estimation
- 6.6.1.. Optimal interpolation and inverse autocorrelation function
- 6.6.2.. Estimation of missing values
- 6.7.. Forecasting with outliers
- 6.8.. Other approaches
- 6.9.. Appendix
- 7.. Automatic Modeling Methods for Univariate Series
- 7.1.. Classical model identification methods
- 7.1.1.. Subjectivity of the classical methods
- 7.1.2.. The difficulties with mixed ARMA models
- 7.2.. Automatic model identification methods
- 7.2.1.. Unit root testing
- 7.2.2.. Penalty function methods
- 7.2.3.. Pattern identification methods
- 7.2.4.. Uniqueness of the solution and the purpose of modeling
- 7.3.. Tools for automatic model identification
- 7.3.1.. Test for the log-level specification
- 7.3.2.. Regression techniques for estimating unit roots
- 7.3.3.. The Hannan--Rissanen method
- 7.3.4.. Liu's filtering method
- 7.4.. Automatic modeling methods in the presence of outliers
- 7.4.1.. Algorithms for automatic outlier detection and correction
- 7.4.2.. Estimation and filtering techniques to speed up the algorithms
- 7.4.3.. The need to robustify automatic modeling methods
- 7.4.4.. An algorithm for automatic model identification in the presence of outliers
- 7.5.. An automatic procedure for the general regression--ARIMA model in the presence of outlierw, special effects, and, possibly, missing observations
- 7.5.1.. Missing observations
- 7.5.2.. Trading day and Easter effects
- 7.5.3.. Intervention and regression effects
- 7.6.. Examples
- 7.7.. Tabular summary
- 8.. Seasonal Adjustment and Signal Extraction Time Series
- 8.1.. Introduction
- 8.2.. Some remarks on the evolution of seasonal adjustment methods
- 8.2.1.. Evolution of the methodologic approach
- 8.2.2.. The situation at present
- 8.3.. The need for preadjustment
- 8.4.. Model specification
- 8.5.. Estimation of the components
- 8.5.1.. Stationary case
- 8.5.2.. Nonstationary series
- 8.6.. Historical or final estimator
- 8.6.1.. Properties of final estimator
- 8.6.2.. Component versus estimator
- 8.6.3.. Covariance between estimators
- 8.7.. Estimators for recent periods
- 8.8.. Revisions in the estimator
- 8.8.1.. Structure of the revision
- 8.8.2.. Optimality of the revisions
- 8.9.. Inference
- 8.9.1.. Optical Forecasts of the Components
- 8.9.2.. Estimation error
- 8.9.3.. Growth rate precision
- 8.9.4.. The gain from concurrent adjustment
- 8.9.5.. Innovations in the components (pseudoinnovations)
- 8.10.. An example
- 8.11.. Relation with fixed filters
- 8.12.. Short-versus long-term trends; measuring economic cycles
- Part II. Advanced Topics in Univariate Time Series
- 9.. Heteroscedastic Models
- 9.1.. The ARCH model
- 9.1.1.. Some simple properties of ARCH models
- 9.1.2.. Weaknesses of ARCH models
- 9.1.3.. Building ARCH models
- 9.1.4.. An illustrative example
- 9.2.. The GARCH Model
- 9.2.1.. An illustrative example
- 9.2.2.. Remarks
- 9.3.. The exponential GARCH model
- 9.3.1.. An illustrative example
- 9.4.. The CHARMA model
- 9.5.. Random coefficient autoregressive (RCA) model
- 9.6.. Stochastic volatility model
- 9.7.. Long-memory stochastic volatility model
- 10.. Nonlinear Time Series Models: Testing and Applications
- 10.1.. Introduction
- 10.2.. Nonlinearity tests
- 10.2.1.. The test
- 10.2.2.. Comparison and application
- 10.3.. The Tar model
- 10.3.1.. U.S. real GNP
- 10.3.2.. Postsample forecasts and discussion
- 10.4.. Concluding remarks
- 11.. Bayesian Time Series Analysis
- 11.1.. Introduction
- 11.2.. A general univariate time series model
- 11.3.. Estimation
- 11.3.1.. Gibbs sampling
- 11.3.2.. Griddy Gibbs
- 11.3.3.. An illustrative example
- 11.4.. Model discrimination
- 11.4.1.. A mixed model with switching
- 11.4.2.. Implementation
- 11.5.. Examples
- 12.. Nonparametric Time Series Analysis: Nonparametric Regression, Locally Weighted Regression, Autoregression, and Quantile Regression
- 12.1. Introduction
- 12.2. Nonparametric regression
- 12.3. Kernel estimation in time series
- 12.4. Problems of simple kernel estimation and restricted approaches
- 12.5. Locally weighted regression
- 12.6. Applications of locally weighted regression to time series
- 12.7. Parameter selection
- 12.8. Time series decomposition with locally weighted regression
- 13.. Neural Network Models
- 13.1.. Introduction
- 13.2.. The multilayer perceptron
- 13.3.. Autoregressive neural network models
- 13.3.1.. Example: Sunspot series
- 13.4.. The recurrent perceptron
- 13.4.1.. Examples of recurrent neural network models
- 13.4.2.. A unifying view
- Part III. Multivariate Time Series
- 14.. Vector ARMA Models
- 14.1.. Introduction
- 14.2.. Transfer function or unidirectional models
- 14.3.. The vector ARMA model
- 14.3.1.. Some simple examples
- 14.3.2.. Relationship to transfer function model
- 14.3.3.. Cross-covariance and correlation matrices
- 14.3.4.. The partial autoregression matrices
- 14.4.. Model building strategy for multiple time series
- 14.4.1.. Tentative specification
- 14.4.2.. Estimation
- 14.4.3.. Diagnostic checking
- 14.5.. Analyses of three examples
- 14.5.1.. The SCC data
- 14.5.2.. The gas furnace data
- 14.5.3.. The census housing data
- 14.6.. Structural analysis of multivariate time series
- 14.6.1.. A canonical analysis of multiple time series
- 14.7.. Scalar component models in multiple time series
- 14.7.1.. Scalar component models
- 14.7.2.. Exchangeable models and overparameterization
- 14.7.3.. Model specification via canonical correlation analysis
- 14.7.4.. An illustrative example
- 14.7.5.. Some further remarks
- 15.. Cointegration in the VAR Model
- 15.1.. Introduction
- 15.1.1.. Basic definitions
- 15.2.. Solving autoregressive equations
- 15.2.1.. Some examples
- 15.2.2.. An inversion theorem for matrix polynomials
- 15.2.3.. Granger's representation
- 15.2.4.. Prediction
- 15.3.. The statistical model for I(1) variables
- 15.3.1.. Hypotheses on cointegrating relations
- 15.3.2.. Estimation of cointegrating vectors and calculation of test statistics
- 15.3.3.. Estimation of [beta] under restrictions
- 15.4.. Asymptotic theory
- 15.4.1.. Asymptotic results
- 15.4.2.. Test for cointegrating rank
- 15.4.3.. Asymptotic distribution of [beta] and test for restrictions on [beta]
- 15.5.. Various applications of the cointegration model
- 15.5.1.. Rational expectations
- 15.5.2.. Arbitrage pricing theory
- 15.5.3.. Seasonal cointegration
- 16.. Identification of Linear Dynamic Multiinput/Multioutput Systems
- 16.1.. Introduction and problem statement
- 16.2.. Representations of linear systems
- 16.2.1.. Input/output representations
- 16.2.2.. Solutions of linear vector difference equations (VDEs)
- 16.2.3.. ARMA and state-space representations
- 16.3.. The structure of state-space systems
- 16.4.. The structure of ARMA systems
- 16.5.. The realization of state-space systems
- 16.5.1.. General structure
- 16.5.2.. Echelon forms
- 16.6.. The realization of ARMA systems
- 16.7.. Parametrization
- 16.8.. Estimation of real-valued parameters
- 16.9.. Dynamic specification
- Index
