Time series analysis and its applications : with R examples.
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| Author / Creator: | Shumway, Robert H. |
|---|---|
| Edition: | 2nd ed. |
| Imprint: | New York : Springer, c2006. |
| Description: | xiii, 575 p. ; 25 cm. |
| Language: | English |
| Series: | Springer texts in statistics |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/6002318 |
Table of Contents:
- 1. Characteristics of Time Series
- 1.1. Introduction
- 1.2. The Nature of Time Series Data
- 1.3. Time Series Statistical Models
- 1.4. Measures of Dependence: Autocorrelation and Cross-Correlation
- 1.5. Stationary Time Series
- 1.6. Estimation of Correlation
- 1.7. Vector-Valued and Multidimensional Series
- Problems
- 2. Time Series Regression and Exploratory Data Analysis
- 2.1. Introduction
- 2.2. Classical Regression in the Time Series Context
- 2.3. Exploratory Data Analysis
- 2.4. Smoothing in the Time Series Context
- Problems
- 3. ARIMA Models
- 3.1. Introduction
- 3.2. Autoregressive Moving Average Models
- 3.3. Difference Equations
- 3.4. Autocorrelation and Partial Autocorrelation Functions
- 3.5. Forecasting
- 3.6. Estimation
- 3.7. Integrated Models for Nonstationary Data
- 3.8. Building ARIMA Models
- 3.9. Multiplicative Seasonal ARIMA Models
- Problems
- 4. Spectral Analysis and Filtering
- 4.1. Introduction
- 4.2. Cyclical Behavior and Periodicity
- 4.3. The Spectral Density
- 4.4. Periodogram and Discrete Fourier Transform
- 4.5. Nonparametric Spectral Estimation
- 4.6. Multiple Series and Cross-Spectra
- 4.7. Linear Filters
- 4.8. Parametric Spectral Estimation
- 4.9. Dynamic Fourier Analysis and Wavelets
- 4.10. Lagged Regression Models
- 4.11. Signal Extraction and Optimum Filtering
- 4.12. Spectral Analysis of Multidimensional Series
- Problems
- 5. Additional Time Domain Topics
- 5.1. Introduction
- 5.2. Long Memory ARMA and Fractional Differencing
- 5.3. GARCH Models
- 5.4. Threshold Models
- 5.5. Regression with Autocorrelated Errors
- 5.6. Lagged Regression: Transfer Function Modeling
- 5.7. Multivariate ARMAX Models
- Problems
- 6. State-Space Models
- 6.1. Introduction
- 6.2. Filtering, Smoothing, and Forecasting
- 6.3. Maximum Likelihood Estimation
- 6.4. Missing Data Modifications
- 6.5. Structural Models: Signal Extraction and Forecasting
- 6.6. ARMAX Models in State-Space Form
- 6.7. Bootstrapping State-Space Models
- 6.8. Dynamic Linear Models with Switching
- 6.9. Nonlinear and Non-normal State-Space Models Using Monte Carlo Methods
- 6.10. Stochastic Volatility
- 6.11. State-Space and ARMAX Models for Longitudinal Data Analysis
- Problems
- 7. Statistical Methods in the Frequency Domain
- 7.1. Introduction
- 7.2. Spectral Matrices and Likelihood Functions
- 7.3. Regression for Jointly Stationary Series
- 7.4. Regression with Deterministic Inputs
- 7.5. Random Coefficient Regression
- 7.6. Analysis of Designed Experiments
- 7.7. Discrimination and Cluster Analysis
- 7.8. Principal Components and Factor Analysis
- 7.9. The Spectral Envelope
- Problems
- Appendix A. Large Sample Theory
- A.1. Convergence Modes
- A.2. Central Limit Theorems
- A.3. The Mean and Autocorrelation Functions
- Appendix B. Time Domain Theory
- B.1. Hilbert Spaces and the Projection Theorem
- B.2. Causal Conditions for ARMA Models
- B.3. Large Sample Distribution of the AR(p) Conditional Least Squares Estimators
- B.4. The Wold Decomposition
- Appendix C. Spectral Domain Theory
- C.1. Spectral Representation Theorem
- C.2. Large Sample Distribution of the DFT and Smoothed Periodogram
- C.3. The Complex Multivariate Normal Distribution
- References
- Index
