Forecasting Methods and Applications,9780471532330

Forecasting Methods and Applications

by ; ;
Edition: 3rd
ISBN13: 9780471532330
ISBN10: 0471532339
Format: Hardcover
Pub. Date: 1997-12-29
Publisher(s): Wiley
List Price: $274.33

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Summary

Can You Predict the Future by Looking at the Past? Since accurate forecasting requires more than just inserting historical data into a model, Forecasting: Methods and Applications, 3/e, adopts a managerial, business orientation. Integrated throughout this text is the innovative idea that explaining the past is not adequate for predicting the future. Inside, you will find the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. And you'll develop the necessary skills to meet the increased demand for thoughtful and realistic forecasts. New features in the third edition include: * An emphasis placed on the practical uses of forecasting. * All data sets used in this book are available on the Internet. * Comprehensive coverage provided on both quantitative and qualitative forecasting techniques. * Includes many new developments in forecasting methodology and practice.

Author Biography

Spyros Makridakis is a professor of the University of Nicosia UNIC where he is the Director of the Institute for the Future and an Emeritus Professor of Decision Sciences at INSEAD. Steven C. Wheelwright is the author of Forecasting: Methods and Applications, 3rd Edition, published by Wiley.

Table of Contents

1 THE FORECASTING PERSPECTIVE
1(19)
1/1 Why forecast?
2(4)
1/2 An overview of forecasting techniques
6(7)
1/2/1 Explanatory versus time series forecasting
10(2)
1/2/2 Qualitative forecasting
12(1)
1/3 The basic steps in a forecasting task
13(4)
References and selected bibliography
17(2)
Exercises
19(1)
2 / BASIC FORECASTING TOOLS
20(61)
2/1 Time series and cross-sectional data
21(2)
2/2 Graphical summaries
23(5)
2/2/1 Time plots and time series patterns
24(2)
2/2/2 Seasonal plots
26(1)
2/2/3 Scatterplots
27(1)
2/3 Numerical summaries
28(13)
2/3/1 Univariate statistics
29(5)
2/3/2 Bivariate statistics
34(4)
2/3/3 Autocorrelation
38(3)
2/4 Measuring forecast accuracy
41(11)
2/4/1 Standard statistical measures
42(3)
2/4/2 Out-of-sample accuracy measurement
45(1)
2/4/3 Comparing forecast methods
46(2)
2/4/4 Theil's U-statistic
48(2)
2/4/5 ACF of forecast error
50(2)
2/5 Prediction intervals
52(2)
2/6 Least squares estimates
54(9)
2/6/1 Discovering and describing relationships
59(4)
2/7 Transformations and adjustments
63(8)
2/7/1 Mathematical transformations
63(4)
2/7/2 Calendar adjustments
67(3)
2/7/3 Adjustments for inflation and population changes
70(1)
Appendices
71(3)
2-A Notation for quantitative forecasting
71(1)
2-B Summation sign XXX
72(2)
References and selected bibliography
74(2)
Exercises
76(5)
3 / TIME SERIES DECOMPOSITION
81(54)
3/1 Principles of decomposition
84(5)
3/1/1 Decomposition models
84(3)
3/1/2 Decomposition graphics
87(1)
3/1/3 Seasonal adjustment
88(1)
3/2 Moving averages
89(12)
3/2/1 Simple moving averages
89(5)
3/2/2 Centered moving averages
94(4)
3/2/3 Double moving averages
98(1)
3/2/4 Weighted moving averages
98(3)
3/3 Local regression smoothing
101(5)
3/3/1 Loess
104(2)
3/4 Classical decomposition
106(7)
3/4/1 Additive decomposition
107(2)
3/4/2 Multiplicative decomposition
109(3)
3/4/3 Variations on classical decomposition
112(1)
3/5 Census Bureau methods
113(8)
3/5/1 First iteration
114(4)
3/5/2 Later iterations
118(1)
3/5/3 Extensions to X-12-ARIMA
119(2)
3/6 STL decomposition
121(4)
3/6/1 Inner loop
122(1)
3/6/2 Outer loop
123(1)
3/6/3 Choosing the STL parameters
124(1)
3/6/4 Comparing STL with X-12-ARIMA
124(1)
3/7 Forecasting and decomposition
125(2)
References and selected bibliography
127(3)
Exercises
130(5)
4 / EXPONENTIAL SMOOTHING METHODS
135(50)
4/1 The forecasting scenario
138(3)
4/2 Averaging methods
141(6)
4/2/1 The mean
141(1)
4/2/2 Moving averages
142(5)
4/3 Exponential smoothing methods
147(24)
4/3/1 Single exponential smoothing
147(8)
4/3/2 Single exponential smoothing: an adaptive approach
155(3)
4/3/3 Hot's linear method
158(3)
4/3/4 Holt-Winters' trend and seasonality method
161(8)
4/3/5 Exponential smoothing: Pegels' classification
169(2)
4/4 A comparison of methods
171(3)
4/5 General aspects of smoothing methods
174(5)
4/5/1 Initialization
174(2)
4/5/2 Optimization
176(1)
4/5/3 Prediction intervals
177(2)
References and selected bibliography
179(2)
Exercises
181(4)
5 / SIMPLE REGRESSION
185(55)
5/1 Regression methods
186(1)
5/2 Simple regression
187(21)
5/2/1 Least squares estimation
188(5)
5/2/2 The correlation coefficient
193(3)
5/2/3 Cautions in using correlation
196(2)
5/2/4 Simple regression and the correlation coefficient
198(5)
5/2/5 Residuals, outliers, and influential observations
203(5)
5/2/6 Correlation and causation
208(1)
5/3 Inference and forecasting with simple regression
208(13)
5/3/1 Regression as statistical modeling
209(2)
5/3/2 The F-test for overall significance
211(4)
5/3/3 Confidence intervals for individual coefficients
215(2)
5/3/4 t-tests for individual coefficients
217(1)
5/3/5 Forecasting using the simple regression model
218(3)
5/4 Non-linear relationships
221(7)
5/4/1 Non-linearity in the parameters
222(2)
5/4/2 Using logarithms to form linear models
224(1)
5/4/3 Local regression
224(4)
Appendixes
228(2)
5-A Determining the values of a and b
228(2)
References and selected bibliography
230(1)
Exercises
231(9)
6 / MULTIPLE REGRESSION
240(71)
6/1 Introduction to multiple linear regression
241(22)
6/1/1 Multiple regression model: theory and practice
248(2)
6/1/2 Solving for the regression coefficients
250(1)
6/1/3 Multiple regression and the coefficient of determination
251(1)
6/1/4 The F-test for overall significance
252(3)
6/1/5 Individual coefficients: confidence intervals and t-tests
255(4)
6/1/6 The assumptions behind multiple linear regression models
259(4)
6/2 Regression with time series
263(11)
6/2/1 Checking independence of residuals
265(4)
6/2/2 Time-related explanatory variables
269(5)
6/3 Selecting variables
274(13)
6/3/1 The long list
276(1)
6/3/2 The short list
277(2)
6/3/3 Best subsets regression
279(6)
6/3/4 Stepwise regression
285(2)
6/4 Multicollinearity
287(4)
6/4/1 Multicollinearity when there are two regressors
289(1)
6/4/2 Multicollinearity when there are more than two regressors
289(2)
6/5 Multiple regression and forecasting
291(8)
6/5/1 Example: cross-sectional regression and forecasting
292(2)
6/5/2 Example: time series regression and forecasting
294(4)
6/5/3 Recapitulation
298(1)
6/6 Econometric models
299(4)
6/6/1 The basis of econometric modeling
299(2)
6/6/2 The advantages and drawbacks of econometric methods
301(2)
Appendixes
303(2)
6-A The Durbin-Watson statistic
303(2)
References and selected bibliography
305(1)
Exercises
306(5)
7 / THE BOX-JENKINS METHODOLOGY FOR ARIMA MODELS
311(77)
7/1 Examining correlations in times series data
313(11)
7/1/1 The autocorrelation function
313(4)
7/1/2 A white noise model
317(1)
7/1/3 The sampling distribution of autocorrelations
317(1)
7/1/4 Portmanteau tests
318(2)
7/1/5 The partial autocorrelation coefficient
320(2)
7/1/6 Recognizing seasonality in a time series
322(1)
7/1/7 Example: Pigs slaughtered
322(2)
7/2 Examining stationarity of time series data
324(11)
7/2/1 Removing non-stationarity in a time series
326(3)
7/2/2 A random walk model
329(1)
7/2/3 Tests for statationarity
329(2)
7/2/4 Seasonal differencing
331(3)
7/2/5 Backshift notion
334(1)
7/3 ARIMA models for times series data
335(12)
7/3/1 An autoregressive model of order one
337(2)
7/3/2 A moving average model of order one
339(1)
7/3/3 Higher-order autoregressive models
339(3)
7/3/4 Higher-order moving average models
342(2)
7/3/5 Mixtures: ARIMA models
344(1)
7/3/6 Mixtures: ARIMA models
345(1)
7/3/7 Seasonality and ARIMA models
346(1)
7/4 Identification
347(11)
7/4/1 Example 1: A non-seasonal time series
349(3)
7/4/2 Example 2: A seasonal time series
352(2)
7/4/3 Example 3: A seasonal time series needing transformation
354(3)
7/4/4 Recapitulation
357(1)
7/5 Estimating the parameters
358(2)
7/6 Identification revisited
360(4)
7/6/1 Example 1: Internet usage
362(1)
7/6/2 Example 2: Sales of printing/writing paper
362(2)
7/7 Diagnostic checking
364(2)
7/8 Forecasting with ARIMA models
366(8)
7/8/1 Point forecasts
366(4)
7/8/2 Out-of-sample forecasting
370(1)
7/8/3 The effect of differencing on forecasts
371(1)
7/8/4 ARIMA models used in time series decomposition
372(1)
7/8/5 Equivalances with exponential smoothing models
373(1)
References and selected bibliography
374(3)
Exercises
377(11)
8 / ADVANCED FORECASTING MODELS
388(63)
8/1 Regression with ARIMA errors
390(13)
8/1/1 Modeling procedure
391(2)
8/1/2 Example: Japanese motor vehicle production
393(3)
8/1/3 Example: Sales of petroleum and coal products
396(4)
8/1/4 Forecasting
400(3)
8/2 Dynamic regression models
403(15)
8/2/1 Lagged explanatory variables
403(3)
8/2/2 Koyck model
406(1)
8/2/3 The basic forms of the dynamic regression model
407(2)
8/2/4 Selecting the model order
409(4)
8/2/5 Forecasting
413(2)
8/2/6 Example: Housing starts
415(3)
8/3 Intervention analysis
418(5)
8/3/1 Step-based interventions
419(2)
8/3/2 Pulse-based interventions
421(1)
8/3/3 Further reading
422(1)
8/3/4 Intervention models and forecasting
423(1)
8/4 Multivariate autoregressive models
423(6)
8/5 State space models
429(4)
8/5/1 Some forecasting models in state space form
429(2)
8/5/2 State space forecasting
431(2)
8/5/3 The value of state space models
433(1)
8/6 Non-linear models
433(2)
8/7 Neural network forecasting
435(5)
References and selected bibliography
440(4)
Exercises
444(7)
9 / FORECASTING THE LONG-TERM
451(31)
9/1 Cycles versus long-term trends: forecasting copper prices
452(7)
9/1/1 Forecasting IBM's sales
457(2)
9/2 Long-term mega economic trends
459(7)
9/2/1 Cycles of various durations and depths
461(3)
9/2/2 Implications of extrapolating long-term trends
464(2)
9/3 Analogies
466(6)
9/3/1 The Information versus the Industrial Revolution
467(2)
9/3/2 Five major inventions of the Industrial Revolution and their analogs
469(3)
9/4 Scenario building
472(6)
9/4/1 Businesses: gaining and/or maintaining competitive advantages
472(3)
9/4/2 Jobs, work, and leisure time
475(1)
9/4/3 Physical versus tele-interactions: extent and speed of acceptance
476(2)
References and selected bibliography
478(2)
Exercises
480(2)
10 / JUDGMENTAL FORECASTING AND ADJUSTMENTS
482(32)
10/1 The accuracy of judgmental forecasts
483(9)
10/1/1 The accuracy of forecasts in financial and other markets
484(6)
10/1/2 Non-investment type forecasts
490(2)
10/2 The nature of judgmental biases and limitations
492(11)
10/2/1 Judgmental biases in forecasting
493(3)
10/2/2 Dealing with judgmental biases
496(6)
10/2/3 Conventional wisdom
502(1)
10/3 Combining statistical and judgmental forecasts
503(5)
10/3/1 Arriving at final forecasts during a budget meeting
503(5)
10/4 Conclusion
508(1)
References and selected bibliography
509(3)
Exercises
512(2)
11 / THE USE OF FORECASTING METHODS IN PRACTICE
514(35)
11/1 Surveys among forecasting users
515(10)
11/1/1 Familiarity and satisfaction with major forecasting methods
516(4)
11/1/2 The use of different forecasting methods
520(5)
11/2 Post-sample accuracy: empirical findings
525(7)
11/3 Factors influencing method selection
532(5)
11/4 The combination of forecasts
537(6)
11/4/1 Factors that contribute to making combining work
538(1)
11/4/2 An example of combining
539(4)
References and selected bibliography
543(4)
Exercises
547(2)
12 / IMPLEMENTING FORECASTING: ITS USES, ADVANTAGES, AND LIMITATIONS
549(28)
12/1 What can and cannot be predicted
551(7)
12/1/1 Short-term predictions
553(1)
12/1/2 Medium-term predictions
554(3)
12/1/3 Long-term predictions
557(1)
12/2 Organizational aspects of forecasting
558(9)
12/2/1 Correcting an organization's forecasting problems
561(1)
12/2/2 Types of forecasting problems and their solutions
562(5)
12/3 Extrapolative predictions versus creative insights
567(4)
12/3/1 Hindsight versus foresight
569(2)
12/4 Forecasting in the future
571(4)
12/4/1 Data, information, and forecasts
571(1)
12/4/2 Collective knowledge, experience, and forecasting
572(3)
References and selected bibliography
575(1)
Exercises
576(1)
APPENDIX I / FORECASTING RESOURCES
577(12)
1 Forecasting software
578(5)
1/1 Spreadsheets
578(1)
1/2 Statistics packages
578(1)
1/3 Specialty forecasting packages
579(3)
1/4 Selecting a forecasting package
582(1)
2 Forecasting associations
583(2)
3 Forecasting conferences
585(1)
4 Forecasting journals and newsletters
585(1)
5 Forecasting on the Internet
586(2)
References and selected bibliography
588(1)
APPENDIX II / GLOSSARY OF FORECASTING TERMS
589
APPENDIX III / STATISTICAL TABLES
549(84)
A: Normal probabilities
620(1)
B: Critical values for t-statistic
621(1)
C: Critical values for F-statistic
622(6)
D: Inverse normal table
628(1)
E: Critical values for X(2) statistic
629(1)
F: Values of the Durbin-Watson statistic
630(2)
G: Normally distributed observations
632(1)
AUTHOR INDEX 633(3)
SUBJECT INDEX 636

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