Practical forecasting for managers:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London
Arnold [u.a.]
2001
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 296 S. graph. Darst. |
| ISBN: | 0340762381 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804128442361315328 |
|---|---|
| adam_text | Contents
Chapter 1 Why forecast? 1
Why do we forecast? 1
What can we forecast? 2
Background skills and knowledge 2
How forecasting works 4
Models and their role 4
Skills and knowledge objectives 5
Examinable topics 6
Project, presentation and data analysis skills 7
Concepts, tools or facts for awareness 7
Examples of forecasting tasks 8
Exercises 9
Chapter 2 Planning the forecasting task 10
Defining the goals of a forecasting activity 10
Types of forecasts 10
Background to the forecast 11
Management of forecasting data 13
Assignment of resources 14
Testing and selecting a model 15
Basic rules 17
Trial estimation or modelling period 17
Example of forecasting with transformed variables 18
Validation period 18
Main estimation or modelling period 19
Forecasts 19
Single and multi period forecasts 19
A real example 20
What should we report? 20
Good working habits 21
Exercises 22
Chapter 3 Measuring how well forecasting goals are met. Part 1 23
What is a model? Part 1 23
What are we looking for? Pegels classification 23
Fit of model to data 26
A notation for forecasting 28
Practical Forecasting for Managers
Quantification of the size of deviations 29
How good is a model? Part 1. 30
Desiderata and parsimony. Part 1. 32
Exercises 33
Chapter 4 Data search, gathering, documentation and management 35
Data sources. Data collection and validation. 35
Traditional sources of data 35
Library resources on the Internet 38
Other Internet or machine readable information sources 40
Examples of Internet searches 42
Other machine readable data sources 42
How to reference sources 43
Data edit and imputation 45
Example 46
Data documentation metadata 47
Time point conversions 47
Exercises 48
Chapter 5 Qualitative forecasting: long term 49
Expert opinion 50
Panel s of experts 51
Delphi techniques 52
Cross impact analysis 53
Systems analysis and modelling 55
Simulation and gaming 56
Scenario writing 57
Exercises 58
Chapter 6 Semi quantitative methods 59
Market versus situational forecasting 59
Technological forecasting 59
Surveys 60
Leading indicators 61
Market penetration models 62
Other extrapolation approaches 64
Media analysis the Bhopal example 64
Exercises 66
Contents vii
Chapter 7 Forecasting, Risk, and Strategic Management 68
The role of forecasting in strategic management 68
Risk anticipation 69
Example: Damage to automobile 69
Predicting opportunities 71
Deciding a direction for actions 72
Feedback from management to forecasting 74
Exercises 74
Chapter 8 Measuring how well forecasting goals are met. Part 2 75
How good is a model? Part 2 75
R_squared a unitless comparison of model fit 75
Serial correlation 76
Partial autocorrelations 78
Use of autocorrelations 78
Ljung Box Q statistic and its use 79
Example of use of the Ljung Box statistic 81
The Durbin Watson statistic 84
Desiderata and parsimony. Part 2 85
Exercises 86
Chapter 9 Preliminary data analysis for forecasting 87
Graph the data! 87
Time plot 90
Histogram or other distributional plot 91
Boxplots 93
Scatterplots for possible relationships 93
Quantile plots 94
Colour, patterns, symbols and shading 97
Adjustment of the data 97
Special adjustments for unusual events 98
Imputation for missing or known incorrect data 98
Scaling of data 98
Trading day adjustments 98
First differences 99
Error checks 99
Transforming data to help us understand it 100
Level and variability 101
Managing the graphs we draw 102
Numerical descriptive statistics 104
Using data subsets 104
M Practical Forecasting for Managers
Summary and application to a real data set 107
Exercises 108
Chapter 10 The preliminary forecast: concepts and examples 111
Ruler forecasts 111
Trend equations 113
Example 114
Data subsetting 115
Example 115
Simple seasonal models 115
Trend line calculations for seasonal data 118
Results of simple seasonal techniques 118
Fit 118
Stability of models 119
Deviations and errors 120
Validation 121
Pattern and distribution of residuals 122
Evolution of seasonal factors 124
Housekeeping details 126
Assessment and validation of forecasting models 127
Annual or other long pattern series 129
Forecasts 130
Exercises 131
Chapter 11 A strategy for performing forecasting data analysis 132
Motivations 132
The statistical package approach 133
The spreadsheet approach 134
Special purpose forecasting software 135
Auxiliary tools 136
Other strategic and tactical issues 139
Making the choice 139
Exercises 140
Chapter 12 Forecasting trend and season I: Multiple regression 141
Regression its purposes 141
Regression jargon 142
Dummy variables 144
A real example 146
Estimating regression models 152
Collinearity 154
Contents ix
Example of collinearity in regression 155
Other uses of regression in forecasting 156
Exercises 157
Chapter 13 Forecasting trend and season II: Smoothing methods 158
Why smooth? 158
Moving averages 158
Properties of the Moving Average MA(n) 160
Trended data 160
Learning about smoothing methods 161
Difficulties with Moving Averages 163
Exponential smoothing 164
Double Exponential Smoothing 166
Brown s 1 parameter ES a double exponential smoothing 167
Holt s 2 parameters exponential smoothing 168
Reminders 168
Seasonal series Winters method 170
Other smoothing methods 174
A real example 175
Alternative seasonal forecasting with smoothing 180
Exercises 182
Chapter 14 Forecasting trend and season III: Time series decomposition 183
How does this differ from Winters method? 183
Historical notes 183
Classical time series decomposition 184
An example 187
Further analysis of the irregular component 189
A real example 191
Exercises 192
Chapter 15 ARIMA and related models for forecasting 193
What are ARIMA models? 193
Some useful notation 194
Non seasonal ARIMA models 195
Seasonal models 197
Estimation and use of ARIMA models the Box Jenkins methodology 198
A real example 202
Exercises 206
k Practical Forecasting for Managers
Chapter 16 Using ARIMA models: other issues and examples 208
Transforming a series to stationarity 208
Developing candidate ARIMA models 210
Trial estimation of ARIMA models 212
Selecting working models 213
Computing the right measures of fit and error 214
Alternative seasonal ARIMA modelling 215
Exercises 216
Chapter 17 Comparing and combining forecasts 217
Descriptive comparison 217
Volume of data and information 217
Type of model 218
Quantitative measures 219
Graphical comparison 220
Reporting honestly 221
Combining forecasts 222
A real example 223
Review of quantitative forecasting methods 231
Exercises 232
Chapter 18 Variations on the theme of seasonal adjustment 233
Origins and motivations 233
Goals of decomposition modelling 234
The underlying model additive or multiplicative 234
Massaging the data and metadata 235
Missing data in seasonal series 237
Choosing the trend and seasonal filters 237
Moving medians and SABL Seasonal Adjustment Bell Labs 237
Weighted moving averages 238
Some other considerations 238
Calendar adjustments 238
Variation in the model components 239
Seasonal adjustment for small to medium organizations 239
Exercises 240
Chapter 19 Mixed and extended models 242
Overview 242
Sequential modelling modelling residuals 242
Example: predicting population growth of a city 243
Econometric models and their uses 245
Contents xi
Identification 246
Estimation 247
Simulation and forecasting 247
Revision 247
When are econometric models useful? 247
Sources of advice 248
Dynamic regression models 248
State space modelling 250
Exercises 252
Chapter 20 Nonlinear regression modelling 253
Motivations: nonlinear forecasting models 253
What is a nonlinear model? 253
An example 254
Objective functions, constraints and parameters 256
Methods for nonlinear fitting 257
Tips for choosing model parameters and bounds 259
Growth curve examples 262
Exercises 263
Chapter 21 Artificial neural networks 264
New and improved? 264
Underlying ideas 264
Published examples 265
Assessment of utility of neural networks 266
Exercises 267
Chapter 22 Building the forecast report 268
Grading our own work 268
The one page critique 268
A marking scheme 268
Words, numbers and pictures 269
Generalities and specifics 270
Exercises 271
Appendix: Tips, tricks and scripts 272
1. Importing text data into spreadsheet and statistical software 272
2. Linear interpolation and inverse linear interpolation 272
3. Time point conversions 274
4. Merging seasonal data into a single series 274
5. Separating a time series into seasonal series 276
xii Practical Forecasting for Managers
6. Trading day adjustments 276
7. The ruler forecast 277
8. Long or large (multi page) time plots 278
9. Multiple plots 279
10. Level adjustment for sudden change 280
11. Compressed scale plots 280
12. Trend line calculations for seasonal data 280
13. Equivalence of additive seasonal models using different base seasons 282
14. Drawing Spread Level graphs 284
15. Computing measures of fit 284
Bibliography 286
Index 294
|
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| indexdate | 2024-07-09T18:49:18Z |
| institution | BVB |
| isbn | 0340762381 |
| language | English |
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| spelling | Nash, John C. 1947- Verfasser (DE-588)1045402818 aut Practical forecasting for managers John C. Nash and Mary M. Nash 1. publ. London Arnold [u.a.] 2001 XVI, 296 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Business forecasting Management Prognose (DE-588)4047390-9 gnd rswk-swf Unternehmensplanung (DE-588)4078609-2 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Management (DE-588)4037278-9 gnd rswk-swf Prognose (DE-588)4047390-9 s Management (DE-588)4037278-9 s DE-604 Unternehmensplanung (DE-588)4078609-2 s Prognoseverfahren (DE-588)4358095-6 s DE-188 Nash, Mary M. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009314393&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Nash, John C. 1947- Nash, Mary M. Practical forecasting for managers Business forecasting Management Prognose (DE-588)4047390-9 gnd Unternehmensplanung (DE-588)4078609-2 gnd Prognoseverfahren (DE-588)4358095-6 gnd Management (DE-588)4037278-9 gnd |
| subject_GND | (DE-588)4047390-9 (DE-588)4078609-2 (DE-588)4358095-6 (DE-588)4037278-9 |
| title | Practical forecasting for managers |
| title_auth | Practical forecasting for managers |
| title_exact_search | Practical forecasting for managers |
| title_full | Practical forecasting for managers John C. Nash and Mary M. Nash |
| title_fullStr | Practical forecasting for managers John C. Nash and Mary M. Nash |
| title_full_unstemmed | Practical forecasting for managers John C. Nash and Mary M. Nash |
| title_short | Practical forecasting for managers |
| title_sort | practical forecasting for managers |
| topic | Business forecasting Management Prognose (DE-588)4047390-9 gnd Unternehmensplanung (DE-588)4078609-2 gnd Prognoseverfahren (DE-588)4358095-6 gnd Management (DE-588)4037278-9 gnd |
| topic_facet | Business forecasting Management Prognose Unternehmensplanung Prognoseverfahren |
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