Forecasting and Time Series - An Applied Approach (Bowerman)

Table of Contents

Part I: INTRODUCTION AND REVIEW OF BASIC STATISTICS.

1. An Introduction to Forecasting.

  • Forecasting and Data.

  • Forecasting Methods.

  • Errors in Forecasting.

  • Choosing a Forecasting Technique.

  • An Overview of Quantitative Forecasting Techniques.

2. Basic Statistical Concepts.

  • Populations.

  • Probability.

  • Random Samples and Sample Statistics.

  • Continuous Probability Distributions.

  • The Normal Probability Distribution.

  • The t-Distribution, the F-Distribution, the Chi-Square Distribution.

  • Confidence Intervals for a Population Mean.

  • Hypothesis Testing for a Population Mean.

  • Exercises.

Part II: REGRESSION ANALYSIS.

3. Simple Linear Regression.

  • The Simple Linear Regression Model.

  • The Least Squares Point Estimates. Point Estimates and Point Predictions.

  • Model Assumptions and the Standard Error.

  • Testing the Significance of the Slope and y Intercept.

  • Confidence and Prediction Intervals.

  • Simple Coefficients of Determination and Correlation.

  • An F Test for the Model.

  • Exercises.

4. Multiple Linear Regression.

  • The Linear Regression Model.

  • The Least Squares Estimates, and Point Estimation and Prediction.

  • The Mean Square Error and the Standard Error.

  • Model Utility: R2, Adjusted R2, and the Overall F Test.

  • Testing the Significance of an Independent Variable.

  • Confidence and Prediction Intervals.

  • The Quadratic Regression Model.

  • Interaction.

  • Using Dummy Variables to Model Qualitative Independent Variables.

  • The Partial F Test: Testing the Significance of a Portion of a Regression Model.

  • Exercises.

5. Model Building and Residual Analysis.

  • Model Building and the Effects of Multicollinearity.

  • Residual Analysis in Simple Regression.

  • Residual Analysis in Multiple Regression.

  • Diagnostics for Detecting Outlying and Influential Observations.

  • Exercises.

Part III: TIME SERIES REGRESSION, DECOMPOSITION METHODS, AND EXPONENTIAL SMOOTHING.

6. Time Series Regression.

  • Modeling Trend by Using Polynomial Functions.

  • Detecting Autocorrelation.

  • Types of Seasonal Variation.

  • Modeling Seasonal Variation by Using Dummy Variables and Trigonometric Functions.

  • Growth Curves.

  • Handling First-Order Autocorrelation.

  • Exercises.

7. Decomposition Methods.

  • Multiplicative Decomposition.

  • Additive Decomposition.

  • The X-12-ARIMA Seasonal Adjustment Method.

  • Exercises.

8. Exponential Smoothing.

  • Simple Exponential Smoothing.

  • Tracking Signals.

  • Holt's Trend Corrected Exponential Smoothing.

  • Holt-Winters Methods.

  • Damped Trends and Other Exponential Smoothing Methods.

  • Models for Exponential Smoothing and Prediction Intervals.

  • Exercises.

Part IV: THE BOX-JENKINS METHODOLOGY.

9. Nonseasonal Box-Jenkins Modeling and Their Tentative Identification.

  • Stationary and Nonstationary Time Series.

  • The Sample Autocorrelation and Partial Autocorrelation Functions: The SAC and SPAC.

  • An Introduction to Nonseasonal Modeling and Forecasting.

  • Tentative Identification of Nonseasonal Box-Jenkins Models.

  • Exercises.

10. Estimation, Diagnostic Checking, and Forecasting for Nonseasonal Box-Jenkins Models.

  • Estimation.

  • Diagnostic Checking.

  • Forecasting.

  • A Case Study.

  • Box-Jenkins Implementation of Exponential Smoothing.

  • Exercises.

11. Box-Jenkins Seasonal Modeling.

  • Transforming a Seasonal Time Series into a Stationary Time Series.

  • Three Examples of Seasonal Modeling and Forecasting.

  • Box-Jenkins Error Term Models in Time Series Regression.

  • Exercises.

12. Advanced Box-Jenkins Modeling.

  • The General Seasonal Model and Guidelines for Tentative Identification.

  • Intervention Models.

  • A Procedure for Building a Transfer Function Model.

  • Exercises.

Appendix A: Statistical Tables

Appendix B:

  • Matrix Algebra for Regression Calculations.

  • Matrices and Vectors.

  • The Transpose of a Matrix.

  • Sums and Differences of Matrices.

  • Matrix Multiplication.

  • The Identity Matrix.

  • Linear Dependence and Linear Independence.

  • The Inverse of a Matrix.

  • The Least Squares Point Esimates.

  • The Unexplained Variation and Explained Variation.

  • The Standard Error of the Estimate b.

  • The Distance Value.

  • Using Squared Terms.

  • Using Interaction Terms.

  • Using Dummy Variable.

  • The Standard Error of the Estimate of a Linear Combination of Regression Parameters.

  • Exercises.

Appendix C: References.