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Practical Business Forecasting By Michael Evans, (2002) Publisher: Blackwell Publsihing ISBN: 0-631-220666 Pages:736 pages Price: £27.99 + p&p
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| Contents Table of Contents Book Order form |
Table of Contents
Part I Choosing the Right Type of Forecasting Model
Introduction.
1. Statistics, Econometrics, and Forecasting.
2. Concept of Forecast Accuracy Compared to What?
3. Alternative Types of Forecasts
Point or Interval.
Absolute or Conditional.
Alternative Scenarios Weighed by Probabilities.
4. Some Common Pitfalls in Building Forecasting Equations
Part II Useful Tools for Practical Business Forecasting
Introduction.
5. Types and Sources of Data
6. Collecting Data from the Internet
7. Forecasting Under Uncertainty.
8. Utilizing Graphs and Charts.
9. Mean and Variance.
10. Goodness of Fit Statistics
11. Using the EViews Statistical Package.
12. Utilizing Graphs and Charts.
13. Checklist Before Analyzing Data.
14. Using Logarithms and Elasticities.
Part III The General Linear Regression Model
Introduction.
15. The General Linear Model
16. Uses and Misuses of R-Bar Squared
17. Measuring And Understanding Partial Correlation
18. Testing and Adjusting for Autocorrelation
19. Testing and Adjusting for Heteroscedasticity
20. Getting Started An Example in Eviews
Part IV Additional Topics for Single-Equation Regression Models
Introduction
21. Problems Caused by Multicollinearity.
22. Eliminating or Reducing Spurious Trends
23. Distributed Lags
24. Treatment of Outliers and Issues of Data Adequacy.
25. Uses and Misuses of Dummy Variables
26. Nonlinear Regressions
27. General Steps For Formulating A Multiple Regression Equation.
28. Checking for Normally Distributed Residuals
29. Testing for Equation Stability and Robustness
30. Evaluating Forecast Accuracy.
31. The Effect of Forecasting Errors in the Independent Variables.
32. Comparison with Naïve Models
33. Adjusting the Coefficients of the Model When Forecasting.
34. Buildup of Forecast Error Outside the Sample Period
35. The Basic Time-Series Decomposition Model.
36. Linear and Nonlinear Trends.
37. Methods of Smoothing Data
38. Methods of Seasonal Adjustment
39. Box-Jenkins Philosophy Combining Theoretical and Practical Forecasts.
40. ARIMA Models
41. Stationary and Integrated Series.
42. Identification.
43. Seasonal Factors in ARMA Modeling.
44. Estimation of ARMA Models.
45. Diagnostic Checking and Forecasting.
46. Outline of the Theory of Forecast Combination.
47. Major Sources of Forecast Error.
48. Combining Methods of Nonstructural Estimation.
49. Combining Structural and Nonstructural Methods.
50. The Role of Judgment in Forecasting
51. The Role of Consensus Forecasts.
52. Adjusting Constant Terms and Slope Coefficients
53. Combining Forecasts Summary.
54. Organizing the Sales Forecasting Procedure.
55. Endogenous and Exogenous Variables in Sales Forecasting
56. The Role of Judgment
57. Presenting Sales Forecasts
58. Nonparametric Methods of Long-Term Forecasting
59. Statistical Methods of Determining Nonlinear Trends Nonlinear Growth and Decline, Logistics, and Saturation
60. Predicting Trends Where Cyclical Influences are Important.
61. Projecting Long-Run Trends in Real Growth.
62. Forecasting Very Long-Range Trends Population and Natural Resource Trends
63. Simultaneity Bias in a Single Equation.
64. Estimating Simultaneous Equation Models.
65. Further Issues in Simultaneous Equation Model Forecasting.
66. Summary.
67. Structural vs VAR Models.
68. Solving Structural Macroeconomic Models
69. A Prototype Macroeconomic Model
70. Simulating the Model.
71. Preparing the Model for Forecasting
72. Using the Leading Indicators for Macroeconomic Forecasting.
73. Using Indexes of Consumer and Business Sentiment for Forecasting.
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