Time Series Analysis With Applications in R

Name: Time Series Analysis With Applications in R
Availability: InStock
ISBN: 9780387759586

ISBN-10: 0387759581

ISBN-13: 9780387759586

Edition: 2nd 2008

Authors: Jonathan D. Cryer, Kung-Sik Chan

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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 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…

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: 2.398
Language: English




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