Time Series Analysis With Applications in R

ISBN-10: 0387759581
ISBN-13: 9780387759586
Edition: 2nd 2008
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Description: Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes  More...

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Book details

List price: $97.00
Edition: 2nd
Copyright year: 2008
Publisher: Springer
Publication date: 11/17/2010
Binding: Hardcover
Pages: 491
Size: 7.25" wide x 9.50" long x 1.00" tall
Weight: 2.442
Language: English

Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses.

Introduction
Examples of Time Series
A Model-Building Strategy
Time Series Plots in History
An Overview of the Book
Exercises
Fundamental Concepts
Time Series and Stochastic Processes
Means, Variances, and Covariances
Stationarity
Summary
Exercises
Expectation, Variance, Covariance, and Correlation
Trends
Deterministic Versus Stochastic Trends
Estimation of a Constant Mean
Regression Methods
Reliability and Efficiency of Regression Estimates
Interpreting Regression Output
Residual Analysis
Summary
Exercises
Models for Stationary Time Series
General Linear Processes
Moving Average Processes
Autoregressive Processes
The Mixed Autoregressive Moving Average Model
Invertibility
Summary
Exercises
The Stationarity Region for an AR(2) Process
The Autocorrelation Function for ARMA(p,q)
Models for Nonstationary Time Series
Stationarity Through Differencing
ARIMA Models
Constant Terms in ARIMA Models
Other Transformations
Summary
Exercises
The Backshift Operator
Model Specification
Properties of the Sample Autocorrelation Function
The Partial and Extended Autocorrelation Functions
Specification of Some Simulated Time Series
Nonstationarity
Other Specification Methods
Specification of Some Actual Time Series
Summary
Exercises
Parameter Estimation
The Method of Moments
Least Squares Estimation
Maximum Likelihood and Unconditional Least Squares
Properties of the Estimates
Illustrations of Parameter Estimation
Bootstrapping ARIMA Models
Summary
Exercises
Model Diagnostics
Residual Analysis
Overfitting and Parameter Redundancy
Summary
Exercises
Forecasting
Minimum Mean Square Error Forecasting
Deterministic Trends
ARIMA Forecasting
Prediction Limits
Forecasting Illustrations
Updating ARIMA Forecasts
Forecast Weights and Exponentially Weighted Moving Averages
Forecasting Transformed Series
Summary of Forecasting with Certain ARIMA Models
Summary
Exercises
Conditional Expectation
Minimum Mean Square Error Prediction
The Truncated Linear Process
State Space Models
Seasonal Models
Seasonal ARIMA Models
Multiplicative Seasonal ARMA Models
Nonstationary Seasonal ARIMA Models
Model Specification, Fitting, and Checking
Forecasting Seasonal Models
Summary
Exercises
Time Series Regression Models
Intervention Analysis
Outliers
Spurious Correlation
Prewhitening and Stochastic Regression
Summary
Exercises
Time Series Models of Heteroscedasticity
Some Common Features of Financial Time Series
The ARCH(1) Model
GARCH Models
Maximum Likelihood Estimation
Model Diagnostics
Conditions for the Nonnegativity of the Conditional Variances
Some Extensions of the GARCH Model
Another Example: The Daily USD/HKD Exchange Rates
Summary
Exercises
Formulas for the Generalized Portmanteau Tests
Introduction to Spectral Analysis
Introduction
The Periodogram
The Spectral Representation and Spectral Distribution
The Spectral Density
Spectral Densities for ARMA Processes
Sampling Properties of the Sample Spectral Density
Summary
Exercises
Orthogonality of Cosine and Sine Sequences
Estimating the Spectrum
Smoothing the Spectral Density
Bias and Variance
Bandwidth
Confidence Intervals for the Spectrum
Leakage and Tapering
Autoregressive Spectrum Estimation
Examples with Simulated Data
Examples with Actual Data
Other Methods of Spectral Estimation
Summary
Exercises
Tapering and the Dirichlet Kernel
Threshold Models
Graphically Exploring Nonlinearity
Tests for Nonlinearity
Polynomial Models Are Generally Explosive
First-Order Threshold Autoregressive Models
Threshold Models
Testing for Threshold Nonlinearity
Estimation of a TAR Model
Model Diagnostics
Prediction
Summary
Exercises
The Generalized Portmanteau Test for TAR
An Introduction to R
Introduction
Chapter 1 R Commands
Chapter 2 R Commands
Chapter 3 R Commands
Chapter 4 R Commands
Chapter 5 R Commands
Chapter 6 R Commands
Chapter 7 R Commands
Chapter 8 R Commands
Chapter 9 R Commands
Chapter 10 R Commands
Chapter 11 R Commands
Chapter 12 R Commands
Chapter 13 R Commands
Chapter 14 R Commands
Chapter 15 R Commands
New or Enhanced Functions in the TSA Library
Dataset Information
Bibliography
Index

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