Format:
XV, 478 Seiten
,
Illustrationen
ISBN:
0201159112
,
0-201-15911-2
Series Statement:
The advanced book program
Content:
Time Series Analysis: Univariate and Multivariate Methods emphaszies and provides a broad coverage of methodology. This comprehensive book is of interest to a variety of people in the applied sciences who want to know how time series can be used in their areas of research. The book provides examples useful for showing the operational details and purpose of the methods. Thime series Analysis: covers methods extensively, and illustrates them with numerous figures, tables, and examples using many real-life time series data sets; introduces univariate and multivariate time series models and methods which are useful for analyzing, modeling, and forecasting data collected sequentially in time; provides a balanced treatment between theory and applications; is a comprehensive introduction to both time-domain and frequency-domain analyses; and gives extensive coverage of both univariate and multivariate time series methods, including the most recently developed techniques in the field.
Note:
MAB0014.001: AWI S2-96-0707
,
CONTENTS: 1 Overview. - 1.1 Introduction. - 1.2 Examples and Scope of This Book. - 2 Fundamental Concepts. - 2.1 Stochastic Processes. - 2.2 The Autocovariance and Autocarrelation Functions. - 2.3 The Partial Autocarrelation Function. - 2.4 White Noise Processes. - 2.5 Estimation of the Mean, Autocovariances, and Autocarrelations. - 2.5.1 Sample Mean. - 2.5.2 Sample Autocovariance Function. - 2.5.3 Sample Autocarrelation Function. - 2.5.4 Sample Partial Autocarrelation Function. - 2.6 Moving Average and Autoregressive Representations of Time Series Processes. - 2.7 Linear Difference Equations. - Exercises. - 3 Stationary Time Series Models. - 3.1 Autoregressive Processes. - 3.1.1 The First Order Autoregressive AR(1) Process. - 3.1.2 The Second Order Autoregressive AR(2) Process. - 3.1.3 The General pth Order Autoregressive AR(p)Process. - 3.2 Moving Average Processes. - 3.2.1 The First Order Moving Average MA(1) Process. - 3.2.2 The Second Order Moving Average MA(2) Process. - 3.2.3 The General qth Order Moving Average MA(q) Process. - 3.3 The Dual Relationship between AR(p) and MA(q) Processes. - 3.4 Autoregressive Moving Average ARMA(p,q) Processes. - 3.4.1 The General Mixed ARMA(p,q) Process. - 3.4.2 The ARMA(1, 1) Process. - Exercises. - 4 Nonstationary Time Series Models. - 4.1 Nonstationarity in the Mean. - 4.1.1 Deterministic Trend Models. - 4.1.2 Stochastic Trend Models and Differencing. - 4.2 Autoregressive Integrated Moving Average ARIMA Models. - 4.2.1 The General ARIMA Model. - 4.2.2 The Random Walk Model. - 4.2.3 The ARIMA(0, 1, 1) or IMA(1, 1) Model. - 4.3 Nonstationarity in the Variance and the Autocovariance. - 4.3.1 Variance and Autocovariance of the ARIMA Models. - 4.3.2 Variance Stabilizing Transformations. - Exercises. - 5 Forecasting. - 5.1 lntroduction. - 5.2 Minimum Mean Square Error Forecasts. - 5.2.1 Minimum Mean Square Error Forecasts for ARMA Models. - 5.2.2 Minimum Mean Square Error Forecasts for ARIMA Models. - 5.3 Computation of Forecasts. - 5.4 The ARIMA Forecast as a Weighted Average of Previous Observations. - 5.5 Updating Forecasts. - 5.6 Eventual Forecast Functions. - 5.7 A Numerical Example. - Exercises. - 6 Model ldentification. - 6.1 Steps for Model ldentification. - 6.2 Empirical Examples. - 6.3 Inverse Autocarrelation Function (IACF). - 6.4 Extended Sample Autocarrelation Function and Other ldentification Procedures. - 6.4.1 Extended Sample Autocarrelation Function (ESACF). - 6.4.2 Other ldentification Procedures. - Exercises. - 7 Parameter Estimation, Diagnostic Checking, and Model Selection. - 7.1 The Method of Moments. - 7.2 Maximum Likelihood Method. - 7.2.1 Conditional Maximum Likelihood Estimation. - 7.2.2 Unconditional Maximum Likelihood Estimationand Backcasting Method. - 7.2.3 Exact Likelihood Functions. - 7.3 Nonlinear Estimation. - 7.4 Ordinary Least Squares (OLS) Estimation in Time Series Analysis. - 7.5 Diagnostic Checking. - 7.6 Empirical Examples for Series W1-W7. - 7.7 Model Selection Criteria. - Exercises. - 8 Seasonal Time Series Models. - 8.1 Introduction. - 8.2 Traditional Methods. - 8.2.1 Regression Method. - 8.2.2 Moving Average Method. - 8.3 Seasonal ARIMA Models. - 8.4 Empirical Examples. - Exercises. - 9 Intervention Analysis and Outlier Detection. - 9.1 Intervention Models. - 9.2 Examples of Intervention Analysis. - 9.3 Time Series Outliers. - 9.3.1 Additive and Innovational Outliers. - 9.3.2 Estimation of the Outlier Effect When theTiming of the Outlier ls Known. - 9.3.3 Detection of Outliers Using an Iterative Procedure. - 9.4 Examples of Outlier Analysis. - 9.5 Remarks on Outlier and Intervention Problems. - Exercises. - 10 Fourier Analysis. - 10.1 Introduction. - 10.2 Orthogonal Functions. - 10.3 Fourier Representation of Finite Sequences. - 10.4 Fourier Representation of Periodic Sequences. - 10.5 Fourier Representation of Nonperiodic Sequences - The Discrete-Time Fourier Transform. - 10.6 Fourier Representation of Continuous-Time Functions. - 10.6.1 Fourier Representation of Periodic Functions. - 10.6.2 Fourier Representation of Nonperiodic Functions - The Continuous-Time Fourier Transform. - 10.7 The Fast Fourier Transform. - Exercises. - 11 Spectral Theory of Stationary Processes. - 11.1 The Spectrum. - 11.1.1 The Spectrum and lts Properties. - 11.1.2 The Spectral Representation of Autocovariance Functions - The Spectral Distribution Function. - 11.1.3 Wold's Decomposition of a Stationary Process. - 11.1.4 The Spectral Representation of Stationary Processes. - 11.2 The Spectrum of Same Common Processes. - 11.2.1 The Spectrum and the Autocovariance Generating Function. - 11.2.2 The Spectrum of ARMA Models. - 11.2.3 The Spectrum of the Sum of Two Independent Processes. - 11.2.4 The Spectrum of Seasonal Models. - 11.3 The Spectrum of Linear Filters. - 11.4 Aliasing. - Exercises. - 12 Estimation of the Spectrum. - 12.1 Periodogram Analysis. - 12.1.1 The Periodogram. - 12.1.2 Sampling Properties of the Periodogram. - 12.1.3 Test for Hidden Periodic Components. - 12.2 The Sample Spectrum. - 12.3 The Smoothed Spectrum. - 12.3.1 Smoothing in the Frequency Domain - The Spectral Window. - 12.3.2 Smoothing in the Time Domain - The Lag Window. - 12.3.3 Some Commonly Used Windows. - 12.3.4 Approximate Confidence Intervals for Spectral Ordinates. - 12.4 ARMA Spectral Estimation. - Exercises. - 13 Transfer Function Models. - 13.1 Single-Input Transfer Function Models. - 13.1.1 General Concepts. - 13.1.2 Some Typical Impulse Response Functions. - 13.2 The Cross-Correlation Function and Transfer Function Models. - 13.2.1 The Cross-Correlation Function (CCF). - 13.2.2 The Relationship between the Cross-Correlation Function and the Transfer Function. - 13.3 Construction of Transfer Function Models. - 13.3.1 Sample Cross-Correlation Function. - 13.3.2 Identification of Transfer Function Models. - 13.3.3 Estimation of Transfer Function Models. - 13.3.4 Diagnostic Checking of Transfer Function Models. - 13.3.5 An Empirical Example. - 13.4 Forecasting Using Transfer Function Models. - 13.4.1 Minimum Mean Square Error Forecasts for Stationary Input and Output Series. - 13.4.2 Minimum Mean Square Error Forecasts for Nonstationary Input and Output Series. - 13.4.3 An Example. - 13.5 Bivariate Frequency-Domain Analysis. - 13.5.1 Cross-Covariance Generating Functions and the Cross-Spectrum. - 13.5.2 Interpretation of the Cross-Spectral Functions. - 13.5.3 Examples. - 13.5.4 Estimation of the Cross-Spectrum. - 13.6 The Cross-Spectrum and Transfer Function Models. - 13.6.1 Construction of Transfer Function Models through Cross-Spectrum Analysis. - 13.6.2 Cross-Spectral Functions of Transfer Function Models. - 13.7 Multiple Input Transfer Function Models. - Exercises. - 14 Vector Time Series Models. - 14.1 Covariance and Correlation Matrix Functions. - 14.2 Moving Average and Autoregressive Representations of Vector Processes. - 14.3 The Vector Autoregressive Moving Average Process. - 14.3.1 Vector AR(1) Models. - 14.3.2 Vector AR(p) Models. - 14.3.3 Vector MA(1) Models. - 14.3.4 Vector MA(q) Models. - 14.3.5 Vector ARMA(1, 1) Models. - 14.3.6 Remarks on Vector ARMA Representations. - 14.4 Nonstationary Vector Autoregressive Moving Average Models. - 14.5 Identification of Vector Time Series Models. - 14.5.1 Sample Correlation Matrix Function. - 14.5.2 Partial Autoregression Matrices. - 14.5.3 Partial Lag Correlation Matrix Function. - 14.6 Model Fitting and Forecasting. - 14.7 An Empirical Example. - 14.8 Partial Process and Partial Process Correlation Matrices. - 14.8.1 Covariance Matrix Generating Functions. - 14.8.2 Partial Covariance Matrix Generating Function. - 14.8.3 Partial Process Sample Correlation Matrix Functions. - 14.8.4 An Empirical Example - The U. S. Hog Data. - 14.9 Spectral Properties of Vector Processes. - Exercises. - 15 State Space Models and the Kaiman Filter. - 15.1 Introduction. - 15.2 The Relationship between State Space and ARMA models. - 15.3 State Space Model Fitting and Canonic
Language:
English
Keywords:
Lehrbuch
URL:
https://www.gbv.de/dms/hbz/toc/ht003315529.pdf
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