An Introduction to Time Series Analysis and Forecasting

Preface.
Introduction and Overview:
Purpose.
Time Series.
Missing Data.
Sample Size.
Representativeness.
Scope of Application.
Stochastic and Deterministic Processes.
Stationarity.
Methodological Approaches.
Importance.
Notation.
Extrapolative and Decomposition Models:
Introduction.
Goodness-of-Fit Indicators.
Average Techniques.
Exponential Smoothing.
Decomposition Methods.
New Features of Census X-12.
Introduction of Box-Jenkins Time Series Analysis:
Introduction.
The importance of Time Series Analysis Modeling.
Limitations.
Assumptions.
Time Series.
Tests for Nonstationarity.
Stabilizing the Variance.
Structural or Regime Stability.
Strict Stationarity.
Implications of Stationarity.
The Basic ARIMA Model:
Introduction to ARIMA.
Graphical Analysis of Time Series Data.
Basic Formulation of the Autoregressive Integrated Moving Average Model.
The Sample Autocorrelation Function.
The Standard Error of the ACF.
The Bounds of Stationarity and Invertibility.
The Sample Partial Autocorrelation Function.
Bounds of Stationarity and Invertibility Reviewed.
Other Sample Autocorrelation Funcations.
Tentative Identification of Characteristic Patterns of Integrated, Autoregressive, Moving Average, and ARMA Processes.
Seasonal ARIMA Models:
Cyclicity.
Seasonal Nonstationarity.
Seasonal Differencing.
Multiplicative Seasonal Models.
The Autocorrelation Structure of Seasonal ARIMA Models.
Stationarity and Invertibility of Seasonal ARIMA Model.
A Modeling Strategy for the Seasonal ARIMA Model.
Programming Seasonal Multiplicative Box-Jenkins Models.
Alternative Methods of Modeling Seasonality.
The Question of Deterministic or Stochastic Seasonality.
Estimation and Diagnosis:
Introduction.
Estimation.
Diagnosis of the Model.
Metadiagnosis and Forecasting:
Introduction.
Metadiagnosis.
Forecasting with Box-Jenkins Models.
Characteristics of the Optimal Forecast.
Basic Combination of Forecast.
Forecast Evaluation.
Statistical Package Forecast Syntax.
Regression Combination of Forecasts.
Intervention Analysis:
Introduction: Event Interventions and Their Impacts.
Assumptions of the Event Intervention (Impact Model).
Impact Analysis Theory.
Significance Tests for Impulse Response Functions.
Modeling Strategies for Impact Analysis.
Programming Impact Analysis.
Applications of Impact Analysis.
Advantages of Intervention Analysis.
Limitations of Intervention Analysis.
Transfer Function Models:
Definition of a Transfer Function.
Importance.
Theory of the Transfer Function Model.
Modeling Strategies.
Cointegration.
Long-Run and Short-Run Effects in Dynamic Regression.
Basic Characteristics of a Good Time Series Model.
Chapter 10: Autoregressive Error Models:
The Nature of Serial Correlation of Error.
Sources of Autoregressive Error.
Autoregressive Models with Serially Correlated Errors.
Tests for Serial Correlation of Error.
Corrective Algorithms for Regression Models with Autocorrelated Error.
Forecasting with Autocorrelated Error Models.
Programming Regression with Autocorrelated Errors.
Autoregression in Combining Forecasts.
Models with Stochastic Variance.
A Review of Model and Forecast Evaluation:
Model and Forecat Evaluation.
Model Evaluation.
Comparative Forecast Evaluation.
Comparison of Individual Forecast Methods.
Comparison of Combined Forecast Models.
Power Analysis and Sample Size Determination for Well-Known Time Series Models:
Census X-11.
Box-Jenkins Models.
Tests for Nonstationarity.
Intervention Analysis and Transfer Functions.
Regression with Autoregressive Errors.
Conclusion.
Chapter References.
Appendix A.
Glossary.
Index.