Principles of business forecasting:
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Mason, Ohio
South-Western Cengage Learning
2013
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| Edition: | internat. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references |
| Physical Description: | XXII, 506 S. Ill., graph. Darst. 26 cm |
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MARC
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| 245 | 1 | 0 | |a Principles of business forecasting |c Keith Ord ; Robert Fildes |
| 246 | 1 | 3 | |a Business forecasting |
| 250 | |a internat. ed. | ||
| 264 | 1 | |a Mason, Ohio |b South-Western Cengage Learning |c 2013 | |
| 300 | |a XXII, 506 S. |b Ill., graph. Darst. |c 26 cm | ||
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Record in the Search Index
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| adam_text | Preface xvii
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10*
Chapter 11
Chapter 12
Chapter 13
Forecasting, the Why and the How 1
Basic Tools for Forecasting 19
Forecasting Trends: Exponential Smoothing 58
Seasonal Series: Forecasting and Decomposition 98
State-Space Models for Time Series 127
Autoregressive Integrated Moving Average
(ARIMA) Models 151
Simple Linear Regression for Forecasting 204
Multiple Regression for Time Series 241
Model Building 269
Advanced Methods of Forecasting 310
Judgment-Based Forecasts 360
Putting Forecasting Methods to Work 389
Forecasting in Practice 452
Name Index 495
Subject Index 499
The Appendices are located on the textbook companion site. Students:
Visit www.cengagebrain.com and search using ISBN 9780324311273.
Appendix A Basic Statistical Concepts A1
Appendix B Glossary B1
Appendix C Forecasting Software Cl
Note: Topics marked with an are advanced and may be omitted as needed for
introductory courses.
Preface xvii
Chapter 1 Forecasting, the Why and the How 1
Introduction 2
1.1 Why Forecast? 2
Purpose 3
Horizon 3
Information 3
Value 4
Evaluation 4
1.2 What and Why Do Organizations Forecast? 6
1.3 Examples of Forecasting Problems 7
Retail Sales 7
Seasonal Patterns for Retail Sales 8
UK Road Accidents 11
Airline Travel 11
Sports Forecasting: Soccer (AKA Football!) 11
Sports Forecasting: A Cross-sectional Example—Baseball Salaries 13
Stochastic Processes 14
1.4 How to Forecast 14
1.5 Forecasting Step by Step 15
1.6 Computer Packages for Forecasting 16
1.7 Data Sources 16
1.8 The Rest of the Book 17
Summary 17
Minicase 1.1 Inventory Planning 17
Minicase 1.2 Long-Term Growth 17
Minicase 1.3 Sales Forecasting 18
Minicase 1.4 Adjusting for Inflation 18
References 18
Chapter 2 Basic Tools for Forecasting 19
Introduction 20
2.1 Types of Data 20
Use of Large Databases 21
2.2 Time Series Plots 23
Seasonal Plots 24
2.3 Scatterplots 26
VIII
Contents
2.4 Summarizing the Data 29
Notational Conventions 29
Measures of Average 30
Measures of Variation 31
Assessing Variability 32
An Example: Hot Growth Companies 33
2.5 Correlation 35
2.6 Transformations 38
Differences and Growth Rates 38
The Log Transform 39
2.7 How to Measure Forecasting Accuracy 41
Measures of Forecasting Accuracy 42
Measures of Absolute Error 46
2.8 Prediction Intervals 48
Using the Normal Distribution 49
Empirical Prediction Intervals 49
Prediction Intervals: Summary 50
2.9 Basic Principles 51
Summary 52
Exercises 52
Minicase 2.1 Baseball Salaries 55
Minicase 2.2 Whither Walmart? 56
Minicase 2.3 Economic Recessions 57
References 57
Chapter 3 Forecasting Trends: Exponential Smoothing 58
Introduction 59
Software 59
3.1 Method or Model? 59
A Forecasting Model 60
3.2 Extrapolation Methods 61
Extrapolation of the Mean Value 62
Use of Moving Averages 64
3.3 Simple Exponential Smoothing 66
Forecasting with the EWMA, or Simple Exponential Smoothing 68
An Excel Macro for SES 69
The Use of Hold-Out Samples 71
Some General Comments 72
3.4 Linear Exponential Smoothing 73
Basic Structure for LES 74
Updating Relationships 75
Starting Values 76
3.5 Exponential Smoothing with a Damped Trend 79
Choice of Method 81
Contents
IX
3.6 Other Approaches to Trend Forecasting 81
Brown’s Method of Double Exponential Smoothing (DES) 81
SES with (Constant) Drift 82
Tracking Signals 82
Linear Moving Averages 82
3.7 Prediction Intervals 83
3.8 The Use of Transformations 84
The Log Transform 85
Use of Growth Rates 86
The Box-Cox Transformations 87
3.9 Model Selection 89
3.10 Principles for Extrapolative Models 90
Summary 91
Exercises 91
Minicase 3.1 The Growth of Netflix 94
Minicase 3.2 The Evolution of Walmart 94
Minicase 3.3 Volatility in the Dow Jones Index 96
References 96
Appendix 3A Excel Macros 97
Chapter 4 Seasonal Series: Forecasting and Decomposition 98
Introduction 99
4.1 Components of a Time Series 100
4.2 Forecasting Purely Seasonal Series 101
Purely Seasonal Exponential Smoothing 103
4.3 Forecasting Using a Seasonal Decomposition 104
4.4 Pure Decomposition 108
4.5* The Census X-12 Decomposition 110
4.6 The Holt-Winters Seasonal Smoothing Methods 111
The Additive Holt-Winters Method 111
*Starting Values 112
4.7 The Multiplicative Holt-Winters Method 117
* Starting Values 117
Purely Multiplicative Schemes 118
4.8 Weekly Data 118
Multiple Seasonalities 120
4.9 Prediction Intervals 121
4.10 Principles 121
Summary 122
Exercises 122
Minicase 4.1 Walmart Sales 124
Minicase 4.2 Automobile Production 124
Minicase 4.3 U.S. Retail Sales 124
Minicase 4.4 UK Retail Sales 125
Contents
Minicase 4.5 Newspaper Sales 125
References 126
Appendix 4A Excel Macro for Holt-Winters Methods 126
Chapter 5 State-Space Models for Time Series 127
Introduction 128
5.1 A State-Space Model for Simple Exponential Smoothing 128
The Random Walk 130
The Random Error Term 131
5.2 Prediction Intervals for the Local-Level Model 132
5.3 Model Selection 134
Use of a Hold-out Sample 134
Information Criteria 136
Automatic Selection 139
5.4 Outliers 139
5.5 State-Space Modeling Principles 142
Summary 143
Exercises 143
Minicase 5.1 Analysis of UK Retail Sales 144
Minicase 5.2 Prediction Intervals for WFJ Sales 145
References 145
Appendix 5A* Derivation of Forecast Means and Variances 145
Appendix 5B Pegels’ Classification 146
Appendix 5C* State-Space Models for Other Exponential
Smoothing Methods 147
Chapter 6 Autoregressive Integrated Moving Average (ARIMA)
Models 151
Introduction 152
6.1 The Sample Autocorrelation Function 152
Model Assumptions 154
6.2 Autoregressive Moving Average (ARMA) Models 155
The First-Order Autoregressive Model 155
Higher Order Autoregressive Models 157
Pure Moving Average (MA) Models 158
Mixed Autoregressive Moving Average (ARMA) Models 160
6.3 Partial Autocorrelations and Model Selection 160
The Partial Autocorrelation Function (PACF) 160
Model Choice 164
Nonstationary Series 165
The Random Walk 167
6.4 Model Estimation and Selection 171
Should We Assume Stationarity? 173
Use of Information Criteria 176
How Much Differencing? 177
Formal Tests for Differencing 178
Contents
XI
6.5 Model Diagnostics 178
The Ljung-Box Test 178
6.6 Outliers Again 182
6.7 Forecasting with ARIMA Models 184
Prediction Intervals 185
Forecasting Using Transformations 186
6.8 Seasonal ARIMA Models 188
Forecasts for Seasonal Models 192
6.9* State-Space and ARIMA Models 193
From ARIMA to a State-Space Form 194
6.10* GARCH Models 195
The GARCH (1, 1) Model 196
6.11 Principles of ARIMA Modeling 199
ARIMA Models 199
GARCH Models 199
Summary 200
Exercises 200
Minicase 6.1 201
Minicase 6.2 201
References 202
Appendix 6A* Mean and Variance for AR(1) Scheme 202
Chapter 7 Simple Linear Regression for Forecasting 204
Introduction 205
7.1 Relationships Between Variables: Correlation and Causation 206
What Is Regression Analysis? 207
7.2 Fitting a Regression Line by Ordinary Least Squares (OLS) 208
The Method of Ordinary Least Squares (OLS) 210
7.3 A Case Study on the Price of Gasoline 213
Preliminary Data Analysis 214
The Regression Model 216
7.4 How Good Is the Fitted Line? 217
The Standard Error of Estimate 218
The Coefficient of Determination 219
7.5 The Statistical Framework for Regression 220
The Linear Model 220
Parameter Estimates 222
7.6 Testing the Slope 223
P-Values 224
Interpreting the Slope Coefficient 227
Transformations 228
7.7 Forecasting by Using Simple Linear Regression 229
The Point Forecast 229
Prediction Intervals 230
An Approximate Prediction Interval 232
Forecasting More than One Period Ahead 232
XII
Contents
7.8 Forecasting by Using Leading Indicators 234
Summary 234
Exercises 234
Minicase 7.1 Gasoline Prices Revisited 236
Minicase 7.2 Consumer Confidence and Unemployment 236
Minicase 7.3 Baseball Salaries Revisited 236
References 237
Appendix 7A Derivation of Ordinary Least Squares Estimators 237
Appendix 7B Computing P- Values in Excel 239
Appendix 7C Computing Prediction Intervals 240
Chapter 8 Multiple Regression for Time Series 241
Introduction 242
8.1 Graphical Analysis and Preliminary Model Development 242
8.2 The Multiple Regression Model 243
The Method of Ordinary Least Squares (OLS) 244
8.3 Testing the Overall Model 245
The F-test for Multiple Variables 246
ANOVA in Simple Regression 248
S and Adjusted R2 248
8.4 Testing Individual Coefficients 249
Case Study: Baseball Salaries 251
Testing a Group of Coefficients 251
8.5 Checking the Assumptions 253
Analysis of Residuals for Gas Price Data 256
8.6 Forecasting with Multiple Regression 258
The Point Forecast 259
Prediction Intervals 260
Forecasting More than One Period Ahead 261
8.7 Principles 261
Summary 262
Exercises 262
Minicase 8.1 The Volatility of Google Stock 264
Minicase 8.2 Economic Factors in Homicide Rates 265
Minicase 8.3 Forecasting Natural Gas Consumption for the DC
Metropolitan Area 265
Minicase 8.4 Economic Factors in Property Crime 266
Minicase 8.5 U.S. Retail Food Service Sales 266
Minicase 8.6 U.S. Unemployment Rates 267
References 268
Appendix 8A The Durbin-Watson Statistic 268
Chapter 9 Model Building 269
Introduction 270
9.1 Indicator (Dummy) Variables 271
Seasonal Indicators 274
Contents
XIII
9.2 Autoregressive Models 278
9.3 Models with Both Autoregressive and Regression Components 279
9.4 Selection of Variables 281
Forward, Backward, and Stepwise Selection 282
Searching All Possible Models: Best Subset Regression 284
Using a Hold-out Sample to Compare Models 285
A Regression Model with Autoregressive Errors 286
9.5 Multicollinearity and Structural Change 286
Use of Differences 289
Structural Change 290
9.6 Nonlinear Models 292
Polynomial Schemes 293
Nonlinear Transformations 295
Intrinsically Nonlinear Models 296
Changing Variances and the Use of Logarithmic Models 297
9.7 Outliers and Leverage 299
Leverage Points and What to Do About Them 299
The Effects of Outliers 301
The Role of Outliers and Leverage Points: A Summary 303
9.8 Intervention Analysis 304
9.9 An Update on Forecasting 305
9.10 Principles 306
Summary 306
Exercises 307
Minicase 9.1 An Econometric Analysis of Unleaded Gasoline Prices 308
Minicase 9.2 The Effectiveness of Seat-Belt Legislation 308
References 309
Chapter 10* Advanced Methods of Forecasting 310
Introduction 311
10.1 Predictive Classification 311
Evaluating the Accuracy of the Predictions 314
A Comment 317
10.2 Classification and Regression Trees 318
Performance Measures: An Example 319
Computer Ownership Example Revisited 320
10.3 Logistic Regression 322
Issues in Logistic Regression Modeling 325
10.4 Neural Network Methods 326
A Cross-Sectional Neural Network Analysis 329
A Time Series Neural Network Analysis 331
Neural Networks: A Summary 335
10.5 Vector Autoregressive (VAR) Models 336
10.6 Principles 341
Summary 341
Exercises 342
XIV
Contents
Minicase 10.1 KMI BioPharma, Inc.: Biocide 344
References 348
Appendix 10A PcGive Analysis of Oil Prices Data 349
Appendix 10B The Effects of Nonstationary Data 351
Appendix 10C Differencing and Units Roots 353
Appendix 10D Introduction to Cointegration 355
Appendix 10E Modeling with Nonstationary Data: A Summary 358
Chapter 11 Judgment-Based Forecasts 360
Introduction 361
11.1 Judgmental or Quantitative Forecasting? 362
An Appraisal 366
11.2 Judgmental Methods 366
The Single Expert (or Unaided Judgment) 366
Jury of Expert Opinion 367
Sales Force Projections (or Sales Force Composite) 368
Customer Surveys 368
Use of Analogies 369
11.3 The Delphi Method 371
11.4 Assessing Uncertainty Judgmentally 374
Assessing Percentage Points and Probabilities 374
Decomposition 375
Combining Judgmental Forecasts 376
11.5 The Use of Scenarios 377
Role-Playing 379
11.6 Forecasting Using Prediction Markets 380
The Structure of a Prediction Market 380
How Might Prediction Markets Be Used in Business? 381
Usefulness of Prediction Markets 381
11.7 Judgmental Forecasting Principles 382
Summary 384
Exercises 384
References 386
Appendix 11A Delphi Software 388
Chapter 12 Putting Forecasting Methods to Work 389
Introduction 390
12.1 Evaluating a Forecasting Process 390
Evaluating Forecasting Methods: Forecasting Competitions 391
Combining Forecasting Methods 394
12.2 The Role of Forecasting Support Systems 397
Forecast Adjustment 399
12.3 Operations 401
Supply Chain Forecasting and the Bullwhip Effect 403
Hierarchical Forecasting: A Top-down or Bottom-up Approach? 406
Demand Data and Intermittent Demand 408
Operations Forecasting: Summary 410
Contents
xv
12.4 Marketing 410
New Products and Services 412
Long-Term Trends 412
Diffusion Curves: Modeling the Adoption of New Technologies
and Products 415
Market Potential of a New Product or Technology 418
Market Response Models 422
Expert Adjustments and Promotional Effects 427
Implementation Issues 427
12.5 Forecasting Individual Behavior 428
Comparing Models 430
Building Customer Relationship Management Models 433
Appraising Models of Individual Behavior 436
12.6 Macroeconomic Forecasting 438
12.7 Other Applications 440
12.8 Principles 441
Summary 443
Exercises 444
Minicase 12.1 Call Center Planning 446
Minicase 12.2 Costume Jewelry 447
References 448
Chapter 13 Forecasting in Practice 452
Introduction 453
13.1 The Process of Forecasting 453
The Forecasting Task 454
Forecasting Method Selection in Practice 456
Forecasting Method Selection: Which Methods Work Best Under
What Circumstances? 458
Forecast Evaluation and Monitoring 463
A-B-C Analysis 466
13.2 The Organization of Forecasting 468
The Forecaster 468
The Links to the Forecast User 470
The Politics of Forecasting 471
13.3 Dealing with Uncertainty 474
Simulating Uncertainty 477
Understanding the Impact of Uncertainty and Forecast Errors 478
Scenarios as a Means for Understanding Uncertainty 485
Dealing with Irreducible Uncertainty 486
13.4 Improving Forecasting 488
13.5 Principles for Improving Forecasting 490
Summary 490
Exercises 491
Minicase 13.1 The Management of Call-Center Forecasting 492
References 492
Subject Index 499
The Appendices are located on the textbook companion site. Students:
Visit www.cengagebrain.com and search using ISBN 9780324311273.
Appendix A Basic Statistical Concepts A1
Appendix B Glossary B1
Appendix C Forecasting Software Cl
Preface xvii
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10*
Chapter 11
Chapter 12
Chapter 13
Forecasting, the Why and the How 1
Basic Tools for Forecasting 19
Forecasting Trends: Exponential Smoothing 58
Seasonal Series: Forecasting and Decomposition 98
State-Space Models for Time Series 127
Autoregressive Integrated Moving Average
(ARIMA) Models 151
Simple Linear Regression for Forecasting 204
Multiple Regression for Time Series 241
Model Building 269
Advanced Methods of Forecasting 310
Judgment-Based Forecasts 360
Putting Forecasting Methods to Work 389
Forecasting in Practice 452
Name Index 495
Subject Index 499
The Appendices are located on the textbook companion site. Students:
Visit www.cengagebrain.com and search using ISBN 9780324311273.
Appendix A Basic Statistical Concepts A1
Appendix B Glossary B1
Appendix C Forecasting Software Cl
Note: Topics marked with an are advanced and may be omitted as needed for
introductory courses.
Preface xvii
Chapter 1 Forecasting, the Why and the How 1
Introduction 2
1.1 Why Forecast? 2
Purpose 3
Horizon 3
Information 3
Value 4
Evaluation 4
1.2 What and Why Do Organizations Forecast? 6
1.3 Examples of Forecasting Problems 7
Retail Sales 7
Seasonal Patterns for Retail Sales 8
UK Road Accidents 11
Airline Travel 11
Sports Forecasting: Soccer (AKA Football!) 11
Sports Forecasting: A Cross-sectional Example—Baseball Salaries 13
Stochastic Processes 14
1.4 How to Forecast 14
1.5 Forecasting Step by Step 15
1.6 Computer Packages for Forecasting 16
1.7 Data Sources 16
1.8 The Rest of the Book 17
Summary 17
Minicase 1.1 Inventory Planning 17
Minicase 1.2 Long-Term Growth 17
Minicase 1.3 Sales Forecasting 18
Minicase 1.4 Adjusting for Inflation 18
References 18
Chapter 2 Basic Tools for Forecasting 19
Introduction 20
2.1 Types of Data 20
Use of Large Databases 21
2.2 Time Series Plots 23
Seasonal Plots 24
2.3 Scatterplots 26
VIII
Contents
2.4 Summarizing the Data 29
Notational Conventions 29
Measures of Average 30
Measures of Variation 31
Assessing Variability 32
An Example: Hot Growth Companies 33
2.5 Correlation 35
2.6 Transformations 38
Differences and Growth Rates 38
The Log Transform 39
2.7 How to Measure Forecasting Accuracy 41
Measures of Forecasting Accuracy 42
Measures of Absolute Error 46
2.8 Prediction Intervals 48
Using the Normal Distribution 49
Empirical Prediction Intervals 49
Prediction Intervals: Summary 50
2.9 Basic Principles 51
Summary 52
Exercises 52
Minicase 2.1 Baseball Salaries 55
Minicase 2.2 Whither Walmart? 56
Minicase 2.3 Economic Recessions 57
References 57
Chapter 3 Forecasting Trends: Exponential Smoothing 58
Introduction 59
Software 59
3.1 Method or Model? 59
A Forecasting Model 60
3.2 Extrapolation Methods 61
Extrapolation of the Mean Value 62
Use of Moving Averages 64
3.3 Simple Exponential Smoothing 66
Forecasting with the EWMA, or Simple Exponential Smoothing 68
An Excel Macro for SES 69
The Use of Hold-Out Samples 71
Some General Comments 72
3.4 Linear Exponential Smoothing 73
Basic Structure for LES 74
Updating Relationships 75
Starting Values 76
3.5 Exponential Smoothing with a Damped Trend 79
Choice of Method 81
Contents
IX
3.6 Other Approaches to Trend Forecasting 81
Brown’s Method of Double Exponential Smoothing (DES) 81
SES with (Constant) Drift 82
Tracking Signals 82
Linear Moving Averages 82
3.7 Prediction Intervals 83
3.8 The Use of Transformations 84
The Log Transform 85
Use of Growth Rates 86
The Box-Cox Transformations 87
3.9 Model Selection 89
3.10 Principles for Extrapolative Models 90
Summary 91
Exercises 91
Minicase 3.1 The Growth of Netflix 94
Minicase 3.2 The Evolution of Walmart 94
Minicase 3.3 Volatility in the Dow Jones Index 96
References 96
Appendix 3A Excel Macros 97
Chapter 4 Seasonal Series: Forecasting and Decomposition 98
Introduction 99
4.1 Components of a Time Series 100
4.2 Forecasting Purely Seasonal Series 101
Purely Seasonal Exponential Smoothing 103
4.3 Forecasting Using a Seasonal Decomposition 104
4.4 Pure Decomposition 108
4.5* The Census X-12 Decomposition 110
4.6 The Holt-Winters Seasonal Smoothing Methods 111
The Additive Holt-Winters Method 111
*Starting Values 112
4.7 The Multiplicative Holt-Winters Method 117
* Starting Values 117
Purely Multiplicative Schemes 118
4.8 Weekly Data 118
Multiple Seasonalities 120
4.9 Prediction Intervals 121
4.10 Principles 121
Summary 122
Exercises 122
Minicase 4.1 Walmart Sales 124
Minicase 4.2 Automobile Production 124
Minicase 4.3 U.S. Retail Sales 124
Minicase 4.4 UK Retail Sales 125
Contents
Minicase 4.5 Newspaper Sales 125
References 126
Appendix 4A Excel Macro for Holt-Winters Methods 126
Chapter 5 State-Space Models for Time Series 127
Introduction 128
5.1 A State-Space Model for Simple Exponential Smoothing 128
The Random Walk 130
The Random Error Term 131
5.2 Prediction Intervals for the Local-Level Model 132
5.3 Model Selection 134
Use of a Hold-out Sample 134
Information Criteria 136
Automatic Selection 139
5.4 Outliers 139
5.5 State-Space Modeling Principles 142
Summary 143
Exercises 143
Minicase 5.1 Analysis of UK Retail Sales 144
Minicase 5.2 Prediction Intervals for WFJ Sales 145
References 145
Appendix 5A* Derivation of Forecast Means and Variances 145
Appendix 5B Pegels’ Classification 146
Appendix 5C* State-Space Models for Other Exponential
Smoothing Methods 147
Chapter 6 Autoregressive Integrated Moving Average (ARIMA)
Models 151
Introduction 152
6.1 The Sample Autocorrelation Function 152
Model Assumptions 154
6.2 Autoregressive Moving Average (ARMA) Models 155
The First-Order Autoregressive Model 155
Higher Order Autoregressive Models 157
Pure Moving Average (MA) Models 158
Mixed Autoregressive Moving Average (ARMA) Models 160
6.3 Partial Autocorrelations and Model Selection 160
The Partial Autocorrelation Function (PACF) 160
Model Choice 164
Nonstationary Series 165
The Random Walk 167
6.4 Model Estimation and Selection 171
Should We Assume Stationarity? 173
Use of Information Criteria 176
How Much Differencing? 177
Formal Tests for Differencing 178
Contents
XI
6.5 Model Diagnostics 178
The Ljung-Box Test 178
6.6 Outliers Again 182
6.7 Forecasting with ARIMA Models 184
Prediction Intervals 185
Forecasting Using Transformations 186
6.8 Seasonal ARIMA Models 188
Forecasts for Seasonal Models 192
6.9* State-Space and ARIMA Models 193
From ARIMA to a State-Space Form 194
6.10* GARCH Models 195
The GARCH (1, 1) Model 196
6.11 Principles of ARIMA Modeling 199
ARIMA Models 199
GARCH Models 199
Summary 200
Exercises 200
Minicase 6.1 201
Minicase 6.2 201
References 202
Appendix 6A* Mean and Variance for AR(1) Scheme 202
Chapter 7 Simple Linear Regression for Forecasting 204
Introduction 205
7.1 Relationships Between Variables: Correlation and Causation 206
What Is Regression Analysis? 207
7.2 Fitting a Regression Line by Ordinary Least Squares (OLS) 208
The Method of Ordinary Least Squares (OLS) 210
7.3 A Case Study on the Price of Gasoline 213
Preliminary Data Analysis 214
The Regression Model 216
7.4 How Good Is the Fitted Line? 217
The Standard Error of Estimate 218
The Coefficient of Determination 219
7.5 The Statistical Framework for Regression 220
The Linear Model 220
Parameter Estimates 222
7.6 Testing the Slope 223
P-Values 224
Interpreting the Slope Coefficient 227
Transformations 228
7.7 Forecasting by Using Simple Linear Regression 229
The Point Forecast 229
Prediction Intervals 230
An Approximate Prediction Interval 232
Forecasting More than One Period Ahead 232
XII
Contents
7.8 Forecasting by Using Leading Indicators 234
Summary 234
Exercises 234
Minicase 7.1 Gasoline Prices Revisited 236
Minicase 7.2 Consumer Confidence and Unemployment 236
Minicase 7.3 Baseball Salaries Revisited 236
References 237
Appendix 7A Derivation of Ordinary Least Squares Estimators 237
Appendix 7B Computing P- Values in Excel 239
Appendix 7C Computing Prediction Intervals 240
Chapter 8 Multiple Regression for Time Series 241
Introduction 242
8.1 Graphical Analysis and Preliminary Model Development 242
8.2 The Multiple Regression Model 243
The Method of Ordinary Least Squares (OLS) 244
8.3 Testing the Overall Model 245
The F-test for Multiple Variables 246
ANOVA in Simple Regression 248
S and Adjusted R2 248
8.4 Testing Individual Coefficients 249
Case Study: Baseball Salaries 251
Testing a Group of Coefficients 251
8.5 Checking the Assumptions 253
Analysis of Residuals for Gas Price Data 256
8.6 Forecasting with Multiple Regression 258
The Point Forecast 259
Prediction Intervals 260
Forecasting More than One Period Ahead 261
8.7 Principles 261
Summary 262
Exercises 262
Minicase 8.1 The Volatility of Google Stock 264
Minicase 8.2 Economic Factors in Homicide Rates 265
Minicase 8.3 Forecasting Natural Gas Consumption for the DC
Metropolitan Area 265
Minicase 8.4 Economic Factors in Property Crime 266
Minicase 8.5 U.S. Retail Food Service Sales 266
Minicase 8.6 U.S. Unemployment Rates 267
References 268
Appendix 8A The Durbin-Watson Statistic 268
Chapter 9 Model Building 269
Introduction 270
9.1 Indicator (Dummy) Variables 271
Seasonal Indicators 274
Contents
XIII
9.2 Autoregressive Models 278
9.3 Models with Both Autoregressive and Regression Components 279
9.4 Selection of Variables 281
Forward, Backward, and Stepwise Selection 282
Searching All Possible Models: Best Subset Regression 284
Using a Hold-out Sample to Compare Models 285
A Regression Model with Autoregressive Errors 286
9.5 Multicollinearity and Structural Change 286
Use of Differences 289
Structural Change 290
9.6 Nonlinear Models 292
Polynomial Schemes 293
Nonlinear Transformations 295
Intrinsically Nonlinear Models 296
Changing Variances and the Use of Logarithmic Models 297
9.7 Outliers and Leverage 299
Leverage Points and What to Do About Them 299
The Effects of Outliers 301
The Role of Outliers and Leverage Points: A Summary 303
9.8 Intervention Analysis 304
9.9 An Update on Forecasting 305
9.10 Principles 306
Summary 306
Exercises 307
Minicase 9.1 An Econometric Analysis of Unleaded Gasoline Prices 308
Minicase 9.2 The Effectiveness of Seat-Belt Legislation 308
References 309
Chapter 10* Advanced Methods of Forecasting 310
Introduction 311
10.1 Predictive Classification 311
Evaluating the Accuracy of the Predictions 314
A Comment 317
10.2 Classification and Regression Trees 318
Performance Measures: An Example 319
Computer Ownership Example Revisited 320
10.3 Logistic Regression 322
Issues in Logistic Regression Modeling 325
10.4 Neural Network Methods 326
A Cross-Sectional Neural Network Analysis 329
A Time Series Neural Network Analysis 331
Neural Networks: A Summary 335
10.5 Vector Autoregressive (VAR) Models 336
10.6 Principles 341
Summary 341
Exercises 342
XIV
Contents
Minicase 10.1 KMI BioPharma, Inc.: Biocide 344
References 348
Appendix 10A PcGive Analysis of Oil Prices Data 349
Appendix 10B The Effects of Nonstationary Data 351
Appendix 10C Differencing and Units Roots 353
Appendix 10D Introduction to Cointegration 355
Appendix 10E Modeling with Nonstationary Data: A Summary 358
Chapter 11 Judgment-Based Forecasts 360
Introduction 361
11.1 Judgmental or Quantitative Forecasting? 362
An Appraisal 366
11.2 Judgmental Methods 366
The Single Expert (or Unaided Judgment) 366
Jury of Expert Opinion 367
Sales Force Projections (or Sales Force Composite) 368
Customer Surveys 368
Use of Analogies 369
11.3 The Delphi Method 371
11.4 Assessing Uncertainty Judgmentally 374
Assessing Percentage Points and Probabilities 374
Decomposition 375
Combining Judgmental Forecasts 376
11.5 The Use of Scenarios 377
Role-Playing 379
11.6 Forecasting Using Prediction Markets 380
The Structure of a Prediction Market 380
How Might Prediction Markets Be Used in Business? 381
Usefulness of Prediction Markets 381
11.7 Judgmental Forecasting Principles 382
Summary 384
Exercises 384
References 386
Appendix 11A Delphi Software 388
Chapter 12 Putting Forecasting Methods to Work 389
Introduction 390
12.1 Evaluating a Forecasting Process 390
Evaluating Forecasting Methods: Forecasting Competitions 391
Combining Forecasting Methods 394
12.2 The Role of Forecasting Support Systems 397
Forecast Adjustment 399
12.3 Operations 401
Supply Chain Forecasting and the Bullwhip Effect 403
Hierarchical Forecasting: A Top-down or Bottom-up Approach? 406
Demand Data and Intermittent Demand 408
Operations Forecasting: Summary 410
Contents
xv
12.4 Marketing 410
New Products and Services 412
Long-Term Trends 412
Diffusion Curves: Modeling the Adoption of New Technologies
and Products 415
Market Potential of a New Product or Technology 418
Market Response Models 422
Expert Adjustments and Promotional Effects 427
Implementation Issues 427
12.5 Forecasting Individual Behavior 428
Comparing Models 430
Building Customer Relationship Management Models 433
Appraising Models of Individual Behavior 436
12.6 Macroeconomic Forecasting 438
12.7 Other Applications 440
12.8 Principles 441
Summary 443
Exercises 444
Minicase 12.1 Call Center Planning 446
Minicase 12.2 Costume Jewelry 447
References 448
Chapter 13 Forecasting in Practice 452
Introduction 453
13.1 The Process of Forecasting 453
The Forecasting Task 454
Forecasting Method Selection in Practice 456
Forecasting Method Selection: Which Methods Work Best Under
What Circumstances? 458
Forecast Evaluation and Monitoring 463
A-B-C Analysis 466
13.2 The Organization of Forecasting 468
The Forecaster 468
The Links to the Forecast User 470
The Politics of Forecasting 471
13.3 Dealing with Uncertainty 474
Simulating Uncertainty 477
Understanding the Impact of Uncertainty and Forecast Errors 478
Scenarios as a Means for Understanding Uncertainty 485
Dealing with Irreducible Uncertainty 486
13.4 Improving Forecasting 488
13.5 Principles for Improving Forecasting 490
Summary 490
Exercises 491
Minicase 13.1 The Management of Call-Center Forecasting 492
References 492
Subject Index 499
The Appendices are located on the textbook companion site. Students:
Visit www.cengagebrain.com and search using ISBN 9780324311273.
Appendix A Basic Statistical Concepts A1
Appendix B Glossary B1
Appendix C Forecasting Software Cl
|
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| author | Ord, John Keith 1942- |
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| ctrlnum | (OCoLC)844088630 (DE-599)BVBBV040975960 |
| discipline | Wirtschaftswissenschaften |
| edition | internat. ed. |
| format | Book |
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| id | DE-604.BV040975960 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:36:36Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025954057 |
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| physical | XXII, 506 S. Ill., graph. Darst. 26 cm |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | South-Western Cengage Learning |
| record_format | marc |
| spelling | Ord, John Keith 1942- Verfasser (DE-588)13325562X aut Principles of business forecasting Keith Ord ; Robert Fildes Business forecasting internat. ed. Mason, Ohio South-Western Cengage Learning 2013 XXII, 506 S. Ill., graph. Darst. 26 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references Unternehmensplanung (DE-588)4078609-2 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Unternehmensplanung (DE-588)4078609-2 s Prognoseverfahren (DE-588)4358095-6 s b DE-604 Fildes, Robert Sonstige (DE-588)170527492 oth Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025954057&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025954057&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| subject_GND | (DE-588)4078609-2 (DE-588)4358095-6 |
| title | Principles of business forecasting |
| title_alt | Business forecasting |
| title_auth | Principles of business forecasting |
| title_exact_search | Principles of business forecasting |
| title_full | Principles of business forecasting Keith Ord ; Robert Fildes |
| title_fullStr | Principles of business forecasting Keith Ord ; Robert Fildes |
| title_full_unstemmed | Principles of business forecasting Keith Ord ; Robert Fildes |
| title_short | Principles of business forecasting |
| title_sort | principles of business forecasting |
| topic | Business forecasting Unternehmensplanung (DE-588)4078609-2 gnd Prognoseverfahren (DE-588)4358095-6 gnd |
| topic_facet | Business forecasting Unternehmensplanung Prognoseverfahren |
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