Forecasting Methods and Applications

, by Makridakis, Spyros G.; Wheelwright, Steven C.; Hyndman, Rob J.

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ISBN: 9780471532330 | 0471532339
Cover: Hardcover
Copyright: 12/29/1997

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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.

1 THE FORECASTING PERSPECTIVE

(19)

1/1 Why forecast?

(4)

1/2 An overview of forecasting techniques

(7)

1/2/1 Explanatory versus time series forecasting

(2)

1/2/2 Qualitative forecasting

(1)

1/3 The basic steps in a forecasting task

(4)

References and selected bibliography

(2)

Exercises

(1)

2 / BASIC FORECASTING TOOLS

(61)

2/1 Time series and cross-sectional data

(2)

2/2 Graphical summaries

(5)

2/2/1 Time plots and time series patterns

(2)

2/2/2 Seasonal plots

(1)

2/2/3 Scatterplots

(1)

2/3 Numerical summaries

(13)

2/3/1 Univariate statistics

(5)

2/3/2 Bivariate statistics

(4)

2/3/3 Autocorrelation

(3)

2/4 Measuring forecast accuracy

(11)

2/4/1 Standard statistical measures

(3)

2/4/2 Out-of-sample accuracy measurement

(1)

2/4/3 Comparing forecast methods

(2)

2/4/4 Theil's U-statistic

(2)

2/4/5 ACF of forecast error

(2)

2/5 Prediction intervals

(2)

2/6 Least squares estimates

(9)

2/6/1 Discovering and describing relationships

(4)

2/7 Transformations and adjustments

(8)

2/7/1 Mathematical transformations

(4)

2/7/2 Calendar adjustments

(3)

2/7/3 Adjustments for inflation and population changes

(1)

Appendices

(3)

2-A Notation for quantitative forecasting

(1)

2-B Summation sign XXX

(2)

References and selected bibliography

(2)

Exercises

(5)

3 / TIME SERIES DECOMPOSITION

(54)

3/1 Principles of decomposition

(5)

3/1/1 Decomposition models

(3)

3/1/2 Decomposition graphics

(1)

3/1/3 Seasonal adjustment

(1)

3/2 Moving averages

(12)

3/2/1 Simple moving averages

(5)

3/2/2 Centered moving averages

(4)

3/2/3 Double moving averages

(1)

3/2/4 Weighted moving averages

(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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Forecasting Methods and Applications

Summary

Author Biography

Table of Contents

Supplemental Materials