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
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 methods of combining forecasts, model and forecast evaluation, along with a sample size analysis for common time series models. The careful linkage of the theoretical constructs with the practical considerations involved in utilizing the statistical package makes it easy for the user to properly apply these techniques.
