Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packagesSAS 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
|Edition description:||New Edition|
|Product dimensions:||1.19(w) x 6.14(h) x 9.21(d)|
Table of Contents
Introduction and Overview.
Extrapolative Models and Decomposition Models.
Introduction to Box-Jenkins Time Series Analysis.
The Basic ARIMA Model.
Seasonal ARIMA Models.
Estimation and Diagnosis.
Metadiagnosis and Forecasting. Intervention Analysis.
Transfer Function Models.
Autoregressive Error Models.
A Review of Model and Forecast Evaluation.
M. McGee, Power Analysis and Sample Size Determination for Well-Known Time Series Models.