Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SAS and SPSS. The text examines moving average, exponential smoothing, Census X-11 deseasonalization, ARIMA, intervention, transfer function, and autoregressive error models and has brief discussions of ARCH and GARCH models. The book features treatments of forecast improvement with regression and autoregression combination models and model and forecast evaluation, along with a sample size analysis for common time series models to attain adequate statistical power. The careful linkage of the theoretical constructs with the practical considerations involved in utilizing the statistical packages makes it easy for the user to properly apply these techniques.
- Describes principal approaches to time series analysis and forecasting
- Presents examples from public opinion research, policy analysis, political science, economics, and sociology
- Math level pitched to general social science usage
- Glossary makes the material accessible for readers at all levels
Upper level undergraduate and graduate students, professors, and researchers studying: time series analysis and forecasting; longitudinal quantitative analysis; and quantitative policy analysis. Students, professors and researchers in the social sciences, business, management, operations research, engineering, and applied mathematics.
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.
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- © Academic Press 2000
- 27th April 2000
- Academic Press
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Robert A. Yaffee, Ph.D., is a Senior Research Consultant/Statistican in the Statistics and Social Science Group of New York University's Academic Computing Facility as well as a Research Scientist/Statistician at the State University of New York Health Science Center in Brooklyn's Division of Geriatric Psychiatry. He received his Ph.D. in political science from Graduate Faculty of Political and Social Research of The New School for Social Research. He serves as a member of the editorial board of the Journal of Gambling Behavior and was on the Research Faculty of Columbia University's School of Public Health before coming to NYU. He also taught in the Statistical packages in the Computer Science Department and the Empirical Research and Advanced Statistics in the Sociology Department of Hunter College. He has published in the fields of statistics, medical research, and psychology.
New York University, New York, U.S.A.
Monnie McGee, Ph.D. is an Assistant Professor of Mathematics and Statistics at Hunter College. She received her Ph.D. from Rice University and has worked as a bio-statistical consultant for The Rockefeller University and as a computational statistician for Electricité de France.
Hunter College, City University of New York
@qu:"Robert Yaffee has performed an invaluable service to students of time series analysis by preparing an introduction to methods for analyzing time series data that includes examples drawn from the social sciences, and demonstrates how to program the procedures in SPSS and SAS. Introduction to Time Series Analysis and Forecasting will be a standard reference for years to come." @source:--DAVID F. GREENBERG, New York University, New York