Forecasting: methods and applications
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Wiley
1998
|
| Ausgabe: | 3. ed. |
| Schlagworte: | |
| Online-Zugang: | Publisher description Table of Contents Inhaltsverzeichnis |
| Beschreibung: | XIV, 642 Seiten Diagramme |
| ISBN: | 0471532339 9780471532330 047189365X |
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| 264 | 1 | |a New York [u.a.] |b Wiley |c 1998 | |
| 300 | |a XIV, 642 Seiten |b Diagramme | ||
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CONTENTS
1 / THE FORECASTING
PERSPECTIVE I
I/I Why forecast? 2
1/2 An overview of forecasting
techniques 6
1/2/1 Explanatory versus time series
forecasting 10
1/2/2 Qualitative forecasting 12
1/3 The basic steps in a forecasting
task 13
References and selected
bibliography 17
Exercises 19
2 / BASIC FORECASTING
TOOLS 20
2/1 Time series and cross sectional
data 21
2/2 Graphical summaries 23
2/2/1 Time plots and time series
patterns 24
2/2/2 Seasonal plots 26
2/2/3 Scatterplots 27
2/3 Numerical summaries 28
2/3/1 Unlvariate statistics 29
2/3/2 Bivarlate statistics 34
2/3/3 Autocorrelation 38
2/4 Measuring forecast accuracy 41
2/4/1 Standard statistical measures 42
2/4/2 Out of sample accuracy
measurement 45
2/4/3 Comparing forecast methods 46
2/4/4 Theirs U statlstic 48
2/4/5 ACF of forecast error 50
2/5 Prediction intervals 52
2/6 Least squares estimates 54
2/6/1 Discovering and describing
relationships 59
2/7 Transformations and
adjustments 63
2/7/1 Mathematical transformations 63
2/7/2 Calendar adjustments 67
2/7/3 Adjustments for Inflation and
population changes 70
Appendices 71
2 A Notation for Quantitative
forecasting 71
2 B Summation sign X 72
References and selected
bibliography 74
Exercises 76
3/ TIME SERIES
DECOMPOSITION 81
3/1 Principles of decomposition 84
3/1/1 Decomposition models 84
3/1/2 Decomposition graphics 87
3/1/3 Seasonal adjustment 88
3/2 Moving averages 89
3/2/1 Simple moving averages 89
3/2/2 Centered moving averages 94
3/2/3 Double moving averages 98
3/2/4 Weighted moving averages 98
3/3 Local regression smoothing 101
3/3/1 Loess 104
3/4 Classical decomposition 106
3/4/1 Additive decomposition 107
V4/2 Multiplicative decomposition 109
IX
3/4/3 Variations on classical
decomposition 112
3/5 Census Bureau methods 113
3/S/l First Iteration 114
3/5/2 Later Iterations 118
3/5/3 Extensions to X 12 ARIMA 119
3/6 STL decomposition 121
3/6/1 Inner loop 122
V6/2 Outer loop 123
3/6/3 Choosing the STL parameters 124
3/6/4 Comparing STL with
X I2 ARIMA 124
3/7 Forecasting and
decomposition 125
References and selected
bibliography 127
Exercises 130
4/ EXPONENTIAL SMOOTHING
METHODS 135
4/1 The forecasting scenario 138
4/2 Averaging methods 141
4/2/1 The mean 141
4/2/2 Moving averages 142
4/3 Exponential smoothing
methods 147
4/3/1 Single exponential
smoothing 147
4/3/2 Single exponential smoothing:
an adaptive approach 155
4/3/3 Hots linear method 158
4/3/4 Holt Winters' trend and
seasonally method 161
4/3/5 Exponential smoothing: Pegels'
classification 169
4/4 A comparison of methods 171
4/5 General aspects of smoothing
methods 174
x
4/S/l Initialization 174
4/5/2 Optimization 176
4/5/3 Prediction Intervals 177
References and selected
bibliography 179
Exercises 181
5/ SIMPLE REGRESSION 185
5/1 Regression methods 186
5/2 Simple regression 187
5/2/1 Least squares estimation 188
S/2/2 The correlation coefficient 193
5/2/1 Cautions In using correlation 196
S/2/4 Simple regression and the
correlation coefficient 198
5/2/5 Residuals, outliers, and
influential observations 203
5/2/6 Correlation and causation 208
5/3 Inference and forecasting with
simple regression 208
5/3/1 Regression as statistical
modeling 209
5/3/2 The F test for overall
significance 211
5/3/3 Confidence Intervals for Individual
coefficients 215
5/3/4 Mests for Individual
coefficients 217
5/3/5 Forecasting using the simple
regression model 218
5/4 Non linear relationships 221
5/4/1 Non linearity in the
parameters 222
5/4/2 Using logarithms to form linear
models 224
5/4/3 Local regression 224
Appendixes 228
5 A Determining the values of a
and b 228
References and selected
bibliography 230
Exercises 231
6/ MULTIPLE REGRESSION 240
6/1 Introduction to multiple linear
regression 241
6/1/1 Multiple regression model:
theory and practice 248
6//I/2 Solving for the regression
coefficients 250
6/1/3 Multiple regression and the
coefficient of determination 251
6/1/4 The F test for overall
significance 252
6/1/5 Individual coefficients: confidence
Intervals and f tests 255
6/1/6 The assumptions behind multiple
linear regression models 259
6/2 Regression with time series 263
6/2/1 Checking Independence of
residuals 265
6/2/2 Time related explanatory
variables 269
6/3 Selecting variables 274
6/3/1 The long list 276
6/5/2 The short list 277
6/5/3 Best subsets regression 279
6/3/4 Stepwlse regression 285
6/4 Multicollinearity 287
6/4/1 Multicollinearity when there are
two regressors 289
6/4/2 Multicollinearity when there are
more than two regressors 289
6/5 Multiple regression and
forecasting 291
6/5/1 Example: cross sectional
regression and forecasting 292
6/5/2 Example: time series regression
and forecasting 294
6/5/3 Recapitulation 298
6/6 Econometric models 299
6/6/1 The basis of econometric
modeling 299
6/6/2 The advantages and drawbacks
of econometric methods 301
Appendixes 303
6 A The Durbin Watson statistic 303
References and selected
bibliography 305
Exercises 306
7/ THE BOX IENKINS
METHODOLOGY FOR
ARIMA MODELS 311
7/1 Examining correlations in times
series data 313
7/1/1 The autocorrelation function 313
7/1/2 A white noise model 317
7/1/3 The sampling distribution of
autocorrelations 317
7/1/4 Portmanteau tests 318
7/1/5 The partial autocorrelation
coefficient 320
7/1/6 Recognizing seasonally in a
time series 322
7/1/7 Example: Pigs slaughtered 322
7/2 Examining stationarity of time
series data 324
7/2/1 Removing non statlonarlty in a
time series 326
7/2/2 A random walk model 329
7/2/3 Tests for statatlonarlty 329
7/2/4 Seasonal differencing 331
7/2/5 Backshlft notion 334
7/3 ARIMA models for times series
data 335
7/3/1 An autoregressive model of
order one 337
xl
7/3/2 A moving average model of order
one 339
7/3/3 Higher order autoregresslve
models 339
7/3/4 Higher order moving average
models 342
7/3/5 Mixtures: AR1MA models 344
7/3/6 Mixtures: ARIMA models 345
7/3/7 Seasonally and ARIMA
models 346
7/4 Identification 347
7/4/1 Example 1: A non seasonal time
series 349
7/4/2 Example 2: A seasonal time
series 352
7/4/3 Example 3: A seasonal time series
needing transformation 354
7/4/4 Recapitulation 357
7/5 Estimating the parameters 358
7/6 Identification revisited 360
7/6/1 Example 1: Internet usage 362
7/6/2 Example 2: Sales of printing/
writing paper 362
7/7 Diagnostic checking 364
7/8 Forecasting with ARIMA
models 366
7/8/1 Point forecasts 366
7/8/2 Out of sample forecasting 370
7/8/3 The effect of differencing on
forecasts 371
7/8/4 ARIMA models used in time
series decomposition 372
7/8/5 Eoulvalances with exponential
smoothing models 373
References and selected
bibliography 374
Exercises 377
8 / ADVANCED FORECASTING
MODELS 388
8/1 Regression with ARIMA
errors 390
8/1/1 Modeling procedure 391
8/1/2 Example: lapanese motor vehicle
production 393
8/1/3 Example: Sales of petroleum and
coal products 396
8/1/4 Forecasting 400
8/2 Dynamic regression models 403
8/2/1 lagged explanatory variables 403
8/2/2 Kqyck model 406
8/2/3 The basic forms of the dynamic
regression model 407
8/2/4 Selecting the model order 409
8/2/5 Forecasting 413
8/2/6 Example: Housing starts 415
8/3 Intervention analysis 418
8/3/1 Step based Interventions 419
8/3/2 Pulse based Interventions 421
8/3/3 Further reading 422
8/3/4 Intervention models and
forecasting 423
8/4 Multivariate autoregressive
models 423
8/5 State space models 429
8/5/1 Some forecasting models In state
space form 429
8/5/2 State space forecasting 431
8/5/3 The value of state space
models 433
8/6 Non linear models 433
8/7 Neural network forecasting 435
xll
References and selected
bibliography 440
Exercises 444
9/ FORECASTING THE LONG
TERM 451
9/1 Cycles versus long term trends:
forecasting copper prices 452
9/1/1 Forecasting IBM's sales 457
9/2 Long term mega economic
trends 459
9/2/1 Cycles of various durations and
depths 461
9/2/2 Implications of extrapolating
long term trends 464
9/3 Analogies 466
9/3/1 The Information versus the
Industrial Revolution 467
9/3/2 Five major Inventions of the
Industrial Revolution and their
analogs 469
9/4 Scenario building 472
9/4/1 Businesses: gaining and/or
maintaining competitive
advantages 472
9/4/2 Jobs, work, and leisure time 475
9/4/3 Physical versus tele lnteractlons:
extent and speed of
acceptance 476
References and selected
bibliography 478
Exercises 480
10 /JUDGMENTAL FORECASTING
AND ADJUSTMENTS 482
10/1 The accuracy of judgmental
forecasts 483
10/1/1 The accuracy of forecasts In
financial and other markets 484
10/1/2 Non Investment type
forecasts 490
10/2 The nature of judgmental biases
and limitations 492
10/2/1 judgmental biases in
forecasting 493
10/2/2 Dealing with judgmental
biases 4%
10/2/3 Conventional wisdom 502
10/3 Combining statistical and
judgmental forecasts 503
10/3/1 Arriving at final forecasts during
a budget meeting 503
10/4 Conclusion 508
References and selected
bibliography 509
Exercises 512
11 / THE USE OF FORECASTING
METHODS IN PRACTICE
514
I I/I Surveys among forecasting
users 515
11/1/1 Familiarity and satisfaction with
major forecasting methods 516
11/1/2 The use of different forecasting
methods 520
11/2 Post sample accuracy: empirical
findings 525
11/3 Factors influencing method
selection 532
11/4 The combination of forecasts 537
11/4/1 Factors that contribute to making
combining work 538
11/4/2 An example of combining 539
xiii
References and selected
bibliography 543
Exercises 547
12/IMPLEMENTING
FORECASTING: ITS USES,
ADVANTAGES, AND
LIMITATIONS 549
12/1 What can and cannot be
predicted 551
12/1/1 Short term predictions 553
12/1/2 Medium term predictions 554
12/1/3 Long term predictions 557
12/2 Organizational aspects of
forecasting 558
12/2/1 Correcting an organizations
forecasting problems 561
12/2/2 Types of forecasting problems
and their solutions 562
12/3 Extrapolative predictions versus
creative insights 567
12/3/1 Hindsight versus foresight 569
12/4 Forecasting in the future 571
12/4/1 Data. Information, and
forecasts 571
12/4/2 Collective knowledge, experience.
and forecasting 572
References and selected
bibliography 575
Exercises 576
APPENDIX I / FORECASTING
RESOURCES 577
I Forecasting software 578
I/I Spreadsheets 578
1/2 Statistics packages 578
1/3 Specialty forecasting packages 579
1/4 Selecting a forecasting
package 582
2 Forecasting associations 583
3 Forecasting conferences 585
4 Forecasting journals and
newsletters 585
5 Forecasting on the Internet 586
References and selected
bibliography 588
APPENDIX II / GLOSSARY OF
FORECASTING TERMS 589
APPENDIX III / STATISTICAL
TABLES 549
A: Normal probabilities 620
B: Critical values for r statistic 621
C: Critical values for F statistic 622
D: Inverse normal table 628
E: Critical values for )£ statistic 629
F: Values of the Durbin Watson
statistic 630
G: Normally distributed observations 632
AUTHOR INDEX 633
SUBJECT INDEX 636
xtv |
| any_adam_object | 1 |
| author | Makridakis, Spyros G. 1941- Wheelwright, Steven C. 1943- Hyndman, Rob J. |
| author_GND | (DE-588)12870358X (DE-588)128703520 (DE-588)135530091 |
| author_facet | Makridakis, Spyros G. 1941- Wheelwright, Steven C. 1943- Hyndman, Rob J. |
| author_role | aut aut aut |
| author_sort | Makridakis, Spyros G. 1941- |
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| callnumber-sort | HD 230.27 M34 41998 |
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| dewey-full | 338.5/44221 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 338 - Production |
| dewey-raw | 338.5/442 21 |
| dewey-search | 338.5/442 21 |
| dewey-sort | 3338.5 3442 221 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 3. ed. |
| format | Book |
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| id | DE-604.BV011928682 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-29T04:08:16Z |
| institution | BVB |
| isbn | 0471532339 9780471532330 047189365X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008062444 |
| oclc_num | 263948916 |
| open_access_boolean | |
| owner | DE-945 DE-824 DE-703 DE-384 DE-19 DE-BY-UBM DE-N2 DE-83 DE-2070s DE-898 DE-BY-UBR DE-634 DE-188 DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-862 DE-BY-FWS DE-706 |
| owner_facet | DE-945 DE-824 DE-703 DE-384 DE-19 DE-BY-UBM DE-N2 DE-83 DE-2070s DE-898 DE-BY-UBR DE-634 DE-188 DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-862 DE-BY-FWS DE-706 |
| physical | XIV, 642 Seiten Diagramme |
| publishDate | 1998 |
| publishDateSearch | 1998 |
| publishDateSort | 1998 |
| publisher | Wiley |
| record_format | marc |
| spellingShingle | Makridakis, Spyros G. 1941- Wheelwright, Steven C. 1943- Hyndman, Rob J. Forecasting methods and applications Unternehmen (DE-588)4061963-1 gnd Methode (DE-588)4038971-6 gnd Prognose (DE-588)4047390-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Statistik (DE-588)4056995-0 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| subject_GND | (DE-588)4061963-1 (DE-588)4038971-6 (DE-588)4047390-9 (DE-588)4358095-6 (DE-588)4056995-0 (DE-588)4043212-9 |
| title | Forecasting methods and applications |
| title_auth | Forecasting methods and applications |
| title_exact_search | Forecasting methods and applications |
| title_full | Forecasting methods and applications Spyros Makridakis ; Steven C. Wheelwright ; Rob J. Hyndman |
| title_fullStr | Forecasting methods and applications Spyros Makridakis ; Steven C. Wheelwright ; Rob J. Hyndman |
| title_full_unstemmed | Forecasting methods and applications Spyros Makridakis ; Steven C. Wheelwright ; Rob J. Hyndman |
| title_short | Forecasting |
| title_sort | forecasting methods and applications |
| title_sub | methods and applications |
| topic | Unternehmen (DE-588)4061963-1 gnd Methode (DE-588)4038971-6 gnd Prognose (DE-588)4047390-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Statistik (DE-588)4056995-0 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| topic_facet | Unternehmen Methode Prognose Prognoseverfahren Statistik Ökonometrisches Modell |
| url | http://www.loc.gov/catdir/description/wiley031/97044416.html http://www.loc.gov/catdir/toc/onix02/97044416.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008062444&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT makridakisspyrosg forecastingmethodsandapplications AT wheelwrightstevenc forecastingmethodsandapplications AT hyndmanrobj forecastingmethodsandapplications |
Inhaltsverzeichnis
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2350 QP 325 M235(3) |
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