Verfügbarkeit: Forecasting principles and applications

Forecasting principles and applications:

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Bibliographische Detailangaben
Beteilige Person:	DeLurgio, Stephen A. (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Boston, Mass. [u.a.] Irwin McGraw-Hill 1998
Ausgabe:	1. ed.
Schriftenreihe:	The Irwin/MacGraw-Hill series
Schlagwörter:	Mathematisches Modell Forecasting > Mathematical models Forecasting > Statistical methods Prognoseverfahren Lehrbuch
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007894024&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	XXVIII, 802 S. graph. Darst. Diskette (9 cm)
ISBN:	0256134332

Internformat

MARC


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Datensatz im Suchindex

_version_	1819247702942154752
adam_text	Contents PARTI Foundations of Forecasting 1 Planning and Forecasting 3 Introduction 4 Purpose of This Book 4 Information Revolution 4 The Importance of Forecasting 4 Financial and Strategic Importance of Forecasting 5 The Pursuit of Knowledge 6 The Commonality of Forecasting 6 The Management Decision Hierarchy 7 Why Forecast? 9 Forecasts Are Necessary Because of Implementation Lead Times 9 What Is a Forecast? 9 A Forecast Should Be a Point. Range, and Probability Estimate 9 What Should Be Forecast? 11 Dependent versus Independent Demands 11 Forecasting Hierarchy—The Process of Aggregation/Disaggregation 11 Common Time Series Patterns 13 Time Series versus Cross Sectional Analysis 13 Random Patterns 13 Trend Patterns 13 Seasonal Patterns 14 Cyclical Patterns 16 Autocorrelated Patterns 16 Outliers 19 Interventions—Unplanned and Planned Events 19 Modeling Combinations of Patterns 20 Overview of Forecasting Methods 21 Time Series (Univariate) Forecasting Methods 21 Causal (Multivariate) Forecasting Methods 21 Xiv Contents Qualitative Forecasting Methods 22 The Art of Forecasting—Management Intuition and Involvement 23 Model Summary 23 Parts of This Book 25 Forecasting Method Selection—A Preview 26 The Forecasting Process 26 Summary 30 Key Terms 31 Key Formulas 31 Problems 32 Minicases 33 References 34 2 Statistical Fundamentals for Forecasting 36 The Importance of Pattern 37 Descriptive Statistics 37 Descriptive and Graphical Tools 38 Probabilities 38 Probability Distributions in Forecasting 39 Univariate Summary Statistics 41 Predicting Values Using Mean, Median, or Mode 41 Comparisons of Measures 42 Properties of Central Values 42 Mean Forecast Error 43 Measuring Errors—Standard Deviation and MAD 43 Normal Distribution 45 Characteristics of the Normal Distribution 45 Describing All Normal Distributions 47 Prediction Intervals 48 MAD—An Easily Calculated Measure of Scatter 48 A Forecasting Example Using Sales of Product A 49 Frequency Distribution Solution 50 Fitting versus Forecasting 51 Absolute Error Measures 52 Relative Measures of Error 54 Cautions in Using Percentages 56 Other Error Measures 56 Statistical Significance Test for Bias 56 Correlation Measures 58 Correlations and Covariances 59 Correlation—A Relative Measure of Association 60 Correlation Coefficient—Big City Bookstore 61 Statistical Significance of the Correlation Coefficient 62 Cause and Effect 66 Correlation Coefficients Measure Linear Association 66 Autocorrelations and ACF (Ic) 67 ACFs of Random Series 70 Random Series and White Noise 70 Contents XV ACFs of Trending Series 71 ACFs of Seasonal Series 72 Which Measure of Correlation? 73 Summary 76 Key Terms 76 Key Formulas 77 Review Problems Using Your Software 79 Problems 79 Minicases 82 References 84 Appendix 2 A Expected Values, White Noise, and Correlations 84 Appendix 2 B (^ Statistic for White Noise ACF(/t)s 88 3 Simple Linear Regression Analysis 92 Introduction 93 Dependent and Independent Variables 93 Scatter Plots 93 Purposes of Regression Analysis 96 Method of Least Squared Deviations 96 Fitted Residual and Forecasted Error 99 Regression Output 100 Standard Error of Estimate (Syx) 102 Adjusted Coefficient of Determination, (R2) 104 Testing the Significance of Regression Coefficients 106 Analysis of Variance in Regression Analysis 107 Total, Explained, and Unexplained Variance 107 TheF Testof ANOVA 108 F Test and t2 110 Regression Assumptions 110 Assumption 1: The Fitted Relationship Is of the Correct Form 110 Assumption 2: Homoscedasticity of Errors 111 Assumption 3: No Serial Correlation in the True Errors (v s) 112 Assumption 4: The Distribution of Errors about the Regression Line Is Approximately Normally Distributed 112 Assumption 5: The Relationship Includes All Important Variables 114 Assumption 6: No Problems from Highly Correlated Xs 114 Serial and Autocorrelation Problems 114 Durbin Watson Statistic 115 Sampling and Regression Analysis 117 Confidence and Prediction Intervals 118 Standard Error of the Regression Line (Sr) 118 Standard Error of Forecast (Sf) 119 Review of Regression Analysis Steps 120 Cause and Effect 120 Nonlinear Models Using Linear Regression 121 Cautions in Using Nonlinear Relationships 122 Regression Advantages and Disadvantages 125 Summary 126 Key Terms 126 Contents Key Formulas 126 Review Problems Using Your Software 129 Problems 129 Minicases 132 References 132 Appendix 3 A Cross Correlation Coefficients 132 PART II Univariate Methods 4 Simple Smoothing Methods 147 Moving Averages 148 Simple Moving Averages (SMA) 148 Choosing the Best Forecasting Model 150 Optimal Number of Periods in a Moving Average 150 When to Use Simple Moving Averages 151 Weighted Moving Averages (WMA) 151 Limitations of the SMA and the WMA 153 Exponential Smoothing 153 Single Exponential Smoothing (SES) 154 The Smoothing Constant 155 Estimating Alpha 156 Alpha Based on Autocorrelations 156 Alpha Based on Desired Simple Moving Average 156 Alpha Based on Minimum RSE 157 Derivation of Exponential Weights for Past Actuals 157 Seasonal SES—Forecasting U.S. Marriages 159 Adaptive Response Rate Exponential Smoothing (ARRES) 162 TST and Erratic Series 164 Accuracy of ARRES 165 Forecasting Low Value or Erratic Series 165 Patterns in Low Value Series 166 Low Values and Erratic Series 166 Group Patterns in Low Value or Erratic Series 166 Extremely Low Values 166 Summary 166 Key Terms 167 Key Formulas 167 Review Problems Using Your Software 168 Problems 168 Minicases 171 References 172 5 Decomposition Methods and Seasonal Indexes 174 Classical Decomposition Method 175 Contents Interpreting Seasonal Indexes 180 Deseasonalizing Values to Identify Trend Cycle 181 Using Simple Linear Regression to Forecast Trend 181 Steps in Classical Multiplicative Decomposition 182 Additive Decomposition Method 185 Decomposition of Monthly Data 186 Census Method II X 11 190 Decomposition Using Regression Analysis 192 Additive Seasonal Regression Models 193 Multiplicative Seasonal Regression Models 194 Testing the Significance of Seasonal Indexes 195 Advantages 196 Disadvantages 196 Summary 197 Key Terms 197 Key Formulas 197 Review Problems Using Your Software 198 Problems 199 Minicases 202 References 202 6 Trend Seasonal and Holt Winters Smoothing 204 Estimating Trends with Differences 205 Forecasting with Differences 207 Statistical Significance Test for Trend 207 Advantages and Disadvantages of Forecasting with Differences 210 Nonlinear Trends and Second Differences 210 Logarithms 212 Seasonal Differences to Model Seasonality and Trends 212 Double Moving Averages 215 Advantages 217 Disadvantages 217 Brown s Double Exponential Smoothing 218 Starting Values of s; and S, 219 Optimal Smoothing Constant 220 Advantages 220 Disadvantages 221 Holt s Two Parameter Trend Model 221 Advantages 224 Disadvantages 224 Winters Three Parameter Exponental Smoothing 224 Initialization of Starting Values 228 Ongoing Use of the Model 229 Additive and Multiplicative Factors 229 Data Requirements 230 Advantages 230 Disadvantages 231 Trend Dampening 231 Summary 232 Contents Key Formulas 232 Review Problems Using Your Software 234 Problems 235 Minicases 238 References 239 Appendix 6 A Fourier Series Analysis 240 PART HI Untvariate ARIMA Methods 7 Univariate ARIMA Models: Introduction 267 ARIMA Overview 268 Process (Population) versus Realization (Sample) 268 ARIMA Model Building Steps 269 ARIMA Model Assumptions 269 ARIMA Notation 270 ARIMA Processes 270 Autoregressive Process—ARIMA( 1,0,0,) 270 Moving Average Process—ARIMA(0,0,1) 271 Integrated Processes—ARIMA(0,l,0) 273 Deterministic Trend Process—ARIMA(0,1,0) 1 274 ARIMA Model Identification 275 Random Walk and Trend: ACFs and PACFs 276 Autoregressive (1,0,0) ACFs and PACFs 276 Moving Average (0,0,1) ACFs and PACFs 278 White Noise 278 (0,0,0) ACFs and PACFs 278 The (2 Statistic and White Noise Diagnosis 280 Characteristics of a Good Model 280 Time Series Examples 280 White Noise Time Series 281 Daily Stock Prices (STOCKA.DAT) 281 An Autoregressive Time Series (DAIRY.DAT) 284 A Moving Average Time Series (FAD.DAT) 286 Variance Stationarity 290 U.S. Stock Index (1,1,0)1,1 Model 292 The Backshift Operator 298 Integrated Stochastic Process (0,1,0) 299 Autoregressive Processes: ARIMA(/j,0,0) Models 300 Model Relationships: AR(p), l(d), and MA(q) 301 ARIMA (1,0,0) Software Output 302 Moving Average Processes: ARIMA(0,0,^) Models 303 General Moving Average Process: ARIMA(0,0,g) 304 AR(2), MA(2), ARMA( 1,1), and Seasonal Processes 305 ARIMA (p,d,q) Models 310 Parameter Redundancy: Mixed Models 310 Contents Summary 314 Key Terms 314 Key Formulas 314 Review Problems Using Your Software 316 Problems 317 Minicases 319 References 320 Appendix 7 A Useful Statistical Definitions Used in Derivations 321 Appendix 7 B White Noise and Stationary 322 Appendix 7 C Theoretical ACFs for an ARIMA( 1,0,0) Process 322 Appendix 7 D Theoretical ACFs for an ARIM A(0,0,1) Process 323 Appendix 7 E Bounds of Invertibility and Stationarity 324 Appendix 7 F Example ARIMA Data Sets 325 Appendix 7 G PACFs and the Yule Walker Equations 326 8 ARIMA Applications 330 The ARIMA Model Building Process 331 SERIESB.DAT: Common Stock Prices 333 Identification 333 Estimation 334 Diagnosis 334 Forecasting 338 Seasonal Time Series 339 Demand for an Animal Pharmaceutical (PHARMDEM.DAT) 340 Identification 340 Estimation 340 Diagnosis 341 Forecasting 343 California Utility Electricity Demand (MWHRS.DAT) 343 Identification 343 Estimation 348 Diagnosis 348 Transforming Logs to Original Values 351 Trends Using Logs 351 Japan Stock Index 352 Identification (JAPAN.DAT) 352 Estimation 352 Diagnosis 1 352 Diagnosis 2 355 Interpretation: ARIMA( 1,1,0) 1, Trend 356 Interpretation: ARIMA( 1,1,0), No Trend 359 Comparison 359 Summary: Fit and Forecast, Japan Index 361 Summary and Conclusions 361 Identification 362 Estimation 362 Diagnosis 363 Forecasting 363 A Fresh Perspective 363 Contents Key Terms 364 Key Formulas 364 Review Problems Using Your Software 365 Problems 366 Minicases 367 References 367 9 ARIMA Forecast Intervals 369 Conditional and Unconditional ARIMA Forecasts 370 Unconditional Forecasts 370 Conditional Forecast 372 Unconditional Forecast 372 Forecast Mean Squared Error (FMSE) and Standard Error (FSE) 373 General ARIMA Models: Psi Weights i/r 374 EMSE(m) and EFSE(m) Calculations 376 Two Period Ahead Prediction Intervals 377 Three Period Ahead Prediction Intervals 378 General EFSE(m) Value 378 ARIMA Prediction Intervals 379 White Noise Prediction Intervals 379 Autoregressive Prediction Intervals 380 Nonstationary Prediction Intervals 382 Moving Average Prediction Intervals 384 Season Nonstationary Prediction Intervals 385 Other Prediction Intervals 388 Summary 392 Key Terms 392 Key Formulas 392 Review Problems Using Your Software 394 Problems 393 Minicases 396 References 396 PART IV Multivariate/Causal Methods 10 Multiple Regression of Time Series 401 Linear Multiple Regression Models 402 General Multiple Regression Model 402 Specific Multiple Regression Model 402 Adjusted Coefficient of Determination R2 405 Partial(Net) Regression Coefficients 405 Regression Plane 407 Multiple Regression Modeling Process 409 Multicollinearity 409 Contents Multicollinearity Solutions 412 Example Multicollinearity Problem (MULT.DAT) 413 Partial F Test for Determining Inclusion of Variables 414 Serial Correlation Problems 416 Forecasting 420 Cochrane Orcutt Iterative Least Squares (COILS) 420 Cochrane Orcutt Example #2 421 Stock Index Analysis Using COILS 422 First Order Differences 424 Elasticities and Logarithmic Relationships 425 Heteroscedasticity 426 Incorrect Functional Form 426 Goldfeld Quandt Test 428 Weighted Least Squares 430 Generalized Least Squares 433 Beta Coefficients 434 Dichotomous (Dummy) Variables 434 Event Influence 435 Changes in the Constant 435 Changes in the Slopes 436 Prediction and Confidence Invervals 437 Parsimony and Data Requirements 438 Nonrepresentative Samples 439 Representative Samples 439 Maximizing^2 441 Automated Regression Modeling 441 Summary and Conclusions 441 Key Terms 442 Key Formulas 442 Review Problems Using Your Software 444 Problems 444 Minicases 448 References 449 Appendix 10 A Deriving Normal Equations and Regression Coefficients 450 11 Econometric Methods 452 Biased Single Equation Relationships 453 Recursive versus Nonrecursive Methods 454 Recursive System of Structural Equations 454 A Simple Recursive System 454 Endogenous. Exogenous, and Intervening Variables 455 Path Coefficients 456 Total, Direct, and Indirect Influences 457 General Assumptions of Structural Equations 460 Weak Causal Order 460 Causal Closure: No Specification Errors 460 Partial Correlation Coefficients 461 Contents Causal Order and Closure 462 Specification Errors 463 Specification Errors, Randomization, and Sample Size Effects 464 Small Sample, Incorrect Specification 465 Large Sample, Incorrect Specification, Hopes of Randomization 465 Small Sample, Correct Specification 467 Large Sample, Correct Specification 467 Granger Causality 469 Granger Causality Test 470 Example of Granger Causality Test 470 Limitations of Granger Causality 472 Simultaneous Equations—Nonrecursive Structural Equations 472 Simultaneity Problems 473 Identification Requirements 473 Two Stage Least Squares, (2SLS) 474 Reduced Form Equations 474 Building Materials Example of 2SLS (BMS.DAT) 475 Quantity Demanded and Supplied—Equilibrium Simultaneity 477 Identification 478 Other Simultaneous Equation Methods 478 Indirect Least Squares (ILS) 478 Serially Correlated Errors and Lagged Dependent Variables 478 Seemingly Unrelated Regression (SUR) Model 478 Three Stage Least Squares (3SLS) 479 Testing for Simultaneity 479 Summary 480 Key Terms 481 Key Formulas 481 Review Problems Using Your Software 484 Problems 484 Minicases 487 References 487 12 ARIMA Intervention Analysis 490 Common Intervention Types 492 Zero Order Intervention Functions 492 Pulse, Abrupt Temporary Impact 492 Abrupt, Temporary Impact 493 Abrupt, Permanent Impact 494 Abrupt, Temporary Impact 494 First Order Intervention Functions 494 Gradual, Permanent Impact 494 Gradual, Temporary Impact 497 Other Examples 498 General Intervention Functions 498 The Noise Model 498 Nonstationary Series 499 Abrupt, Permanent Interventions for Nonstationary Series 500 Contents X Intervention Modeling 502 Steps of Intervention Analysis 502 Zero Order Intervention Model—Demand at a Community Blood Bank (BLOOD.DAT) 504 Noise Model Identification 505 Univariate Analysis of Intervention Series 508 Zero Order Intervention Model—Nonstationary Demand for Bottled Water (WATER.DAT) 508 Modeling the Stock Market Crash of 1987 (SP500I.DAT) 515 First Order Model of Stock Market Crash 518 Interpretation of Natural Log Intervention Models 518 First Order Intervention Function—Advertising Impact on Airline Passengers 522 Intervention Effect 522 Bounds of Stability for First Order Intervention Functions 527 Summary 527 Key Terms 528 Key Formulas 528 Review Problems Using Your Software 530 Problems 530 Minicases 531 References 531 13 Multivariate ARIMA Transfer Functions 533 Transfer Functions 535 Zero Order Transfer Functions (TFs) 535 First Order Transfer Functions 537 Pulses and Shifts 540 Steps of MARIMA Modeling 541 Identification Using Cross Correlation Functions (CCF(fc)) 543 Two Hypothetical Applications 543 Achieving Stationarity of Input and Output 544 Prewhitening the Input Series 545 Pretreating the Output Series 545 Calculating CCFs to Identify r, s, and b 546 Using Residuals of the TF to Identify TF Problems 546 Diagnostics 546 Using Residuals of the TF to Identify Nt 547 New York and London IBM Stock Prices (IBMNYLN.DAT) 548 Identification of NY, = /(LN, _ k) 548 Cross Correlation Function 551 Identification of London Price = /(New York (_ k.) 551 Identifying r, s, and b 552 Estimation and Diagnostics 552 Fit and Forecast 554 Cross Correlations and Identification 556 Several Identification Examples 556 Ambiguous Identifications 558 Contents Lumber Sales = /(Advertising) (LUMBERAD.DAT) 558 Achieving Stationarity 558 Tentative Nt for Yt 559 Prewhitening Xt to Identify the TF 560 Estimating and Diagnosing the TF 560 Multiple Input Transfer Functions—Automobile Market Share = /(Advertised quality, Price ratio) 562 Feedback Systems 572 Summary 572 Key Terms 573 Key Formulas 574 Review Problems Using Your Software 575 Problems 576 Minicases 577 References 577 Appendix 13 A Estimating Impulse Response Weights and Initial Coefficients 578 PARTV Cyclical, Qualitative, and Artificial Intelligence Methods 14 Cyclical Forecasting Methods 583 Theory Driven Analysis 584 Why Model Business Cycles? 584 Understanding Cyclical Influences 585 The Phases of Business Cycles 585 Important Economic Indicators 587 Leading, Coincident, and Lagging Indicators 587 Leading Economic Indicators 589 Cyclical Influences and Financial Markets 589 The Fed s Influence 589 Other Economic Indicators 590 Forecasting Recessions 593 Cyclical Forecasting Methods 595 Decomposition of Cyclical Indexes 596 Decomposition Forecasting 600 Cautions with Cyclical Indexes 600 Paired Indicators and Change Analysis 601 Change Measurements 601 Paired Indicators 601 Percentage Ratios 602 Ratio Behavior Explained 604 Ratio of U.S. Composite Coincident and Lagging Indexes 605 Cautions with Indicators and Ratios 607 Other Leading Indicators 608 Pressure Cycles (PC) 608 Contents „, Diffusion Indexes 611 Summary 612 Key Terms 613 Key Formulas 613 Review Problems Using Your Software 614 Problems 614 Minicases 616 References 617 Appendix 14 A Some General Theories about Cycles 619 15 Technological and Qualitative Forecasting Methods: Long Term Forecasting 624 Subjective Forecasting Methods 627 Jury of Executive Opinion 629 Sales Force Composite Methods 630 Marketing Research and Survey Methods 631 Exploratory Forecasting Methods 632 Scenario Analysis 633 Delphi Method 636 Cross Impact Analysis 638 Analogy Methods 638 Trend Analysis 639 Nominal Group Process 639 Case Study Method 639 Analytic Hierarchy Process 641 Normative Forecasting Methods 641 Relevance Trees (RT) 642 Systems Dynamics 643 S Curves of Growth 643 Technological Life Cycles 643 Substitution Curves 645 Modeling Growth Curves 645 Gompertz Curves 646 Logistics (Pearl) Curve 649 Comparing Logistics (Pearl) and Gompertz Curves 651 Stability of Growth Curves 653 Summary 653 Key Terms 654 Key Formulas 655 Review Problems Using Your Software 655 Problems 656 References 658 16 Artificial Neural Networks, Expert Systems, and Genetic Algorithms 662 Practical Implications of ESs. ANNs, and GAs 663 * » a ,, nnrf Ar Ap] fnmnlexitv 663 i Contents Artificial Intelligence (AI)/Expert Systems (ES) 664 Conventional Program Systems versus Expert Systems 664 Purpose of an Expert System 664 Parts of an Expert System 665 Advantages of Expert Systems 665 Disadvantages of Expert Systems 666 Expert System Applications in Forecasting 666 I. Data Entry and Validation Expert System 666 II. Model Selection and Forecasting Expert System 668 III. System Control and Maintenance Expert System 669 Artificial Neural Networks (ANNs) 670 Architecture of ANNs 672 Neurodes 673 ANN Applications 673 Steps in Developing an ANN 674 1. Determine the Structure of the ANN 678 2. Divide the Data into Training and Validation Sets 679 3. Scale All Input Variables 679 4. Set Initial Weights and Start a Training Epoch 680 5. Input Scaled Variables 681 6. Distribute the Scaled Inputs 681 7. Weight and Sum Outputs at Receiving Nodes 681 8. Transform Hidden Inputs to Outputs 681 9. Weight and Sum Hidden Node Outputs at the Output Nodes 682 10. Transform Inputs at the ANN Output Nodes 682 11. Calculate Output Error 682 12. Backpropagate Errors to Adjust Weights 682 13. Continue the Epoch 682 14. Calculate the Epoch RMS 682 15. Judge Out of Sample Validity 683 ; 16. Use the Model in Forecasting 683 j Understanding the Transfer Function 683 The Function of Bias 684 Other Transfer Functions 685 Describing Rates of Change in ANNs 685 Forecasting Quarterly U.S. Marriages 686 \| Scaling Input Variables 687 , Choosing the Target RMS 688 The Results of Training 689 Forecasting the S P 500 691 Sales with an Interaction Effect (INTERACT.DAT) 691 Backpropagation and Training ANNs 693 The Generalized Delta Rule 694 ANN Summary 696 Comparing ANNs and ESs 697 Genetic Algorithms (GA) 698 What Is a Genetic Code? 698 Evolutionary GA Processes 699 A Simple Crossover Example 700 Contents xxvH Summary 701 Key Terms 702 Key Formulas 702 Review Problems Using Your Software or Enclosed Spreadsheets 704 Problems 704 Minicases 706 References 706 Appendix 16 A Mathematics of a Backpropagation Neural Network 708 PART VI Combining, Validation, and Managerial Issues 17 Control, Validation, and Combining Methods 713 Tracking Signal Control Methods 714 Tracking Signals: Detecting Cumulative (Biased) Errors 714 CUSUM, Tracking Signal 715 Tracking Signal: CUSUM,/MAD, 716 Trigg Tracking Signal: SAD(/MAD( 717 An Example Application of TSM, and TST, 717 Autocorrelation Tracking Signal, r( 719 Backward CUSUM V Mask Tracking Signal 720 Choosing a Simple Tracking Signal 722 Reasonableness Tests 723 Combining Forecast Methods 723 Simple Average Combinations 724 Using Weighted Averages 725 Weights Inverse to the Sum of Squared Errors 725 Weights Determined by Regression Analysis 726 Past Research 727 Using Combinations of Forecasts 727 Consistent Forecasts: The One Number Principle 728 Achieving Consistent Forecasts 729 Item versus Cumulative Forecast Accuracy 729 Group Forecasts 729 Assumption of Independence 730 Using Groups in Forecasting Systems 731 Pyramidal Forecasting Systems 731 Validation Methods: Occam s Razor and Parsimony 733 Model Complexity versus Forecast Accuracy 734 Akaike and Schwarz Bayesian Criteria 734 Split Sample and Out of Sample Validation 736 Extreme Value Behavior 736 Jackknife 737 Bootstrap 738 Summary 738 I Key Terms 739 »»• » 1~ tin Contents i Review Problems Using Your Software 741 ) Problems 741 Minicases 745 References 745 18 Method Characteristics, Accuracy, and Data Sources 748 Characteristics of Forecasting Methods 749 Horizon Length (a) 749 Accuracy at Each Horizon (b) 749 Cost of Development (c) 752 Data Period Used (d) 753 Frequency of Revision (e) 755 Type of Application (f) 755 Automation Potential (g) 755 External and Subjective Data (h) 756 Pattern Recognition Capability (i) 756 Number of Observations Required (j) 756 ! In Summary 757 j Forecast Accuracy 758 I Accuracy of Time Series Methods 758 Summary 762 Key Terms 763 Problems 764 Minicases 764 References 765 Appendix A Forecasting Data Sources 766 Appendix B Outlier Detection and Adjustment Procedures 774 Appendix C Student / Distribution 780 Appendix D Areas of the Standard Normal Distribution 781 Appendix E Critical Values of Chi Square 781 Appendix F The F Distribution for a = .05 and a = .01 (Bold) for Many Possible Degrees of Freedom 783 Appendix G Critical Values of the Durbin Watson Test Statistic for a = .05 785 Index 787 \| i
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genre	(DE-588)4123623-3 Lehrbuch gnd-content
genre_facet	Lehrbuch
id	DE-604.BV011707021
illustrated	Illustrated
indexdate	2024-12-20T10:17:14Z
institution	BVB
isbn	0256134332
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-007894024
oclc_num	633483178
open_access_boolean
owner	DE-739 DE-945 DE-20 DE-188 DE-521
owner_facet	DE-739 DE-945 DE-20 DE-188 DE-521
physical	XXVIII, 802 S. graph. Darst. Diskette (9 cm)
publishDate	1998
publishDateSearch	1998
publishDateSort	1998
publisher	Irwin McGraw-Hill
record_format	marc
series2	The Irwin/MacGraw-Hill series
spellingShingle	DeLurgio, Stephen A. Forecasting principles and applications Mathematisches Modell Forecasting -- Mathematical models Forecasting -- Statistical methods Prognoseverfahren (DE-588)4358095-6 gnd
subject_GND	(DE-588)4358095-6 (DE-588)4123623-3
title	Forecasting principles and applications
title_auth	Forecasting principles and applications
title_exact_search	Forecasting principles and applications
title_full	Forecasting principles and applications Stephen A. DeLurgio
title_fullStr	Forecasting principles and applications Stephen A. DeLurgio
title_full_unstemmed	Forecasting principles and applications Stephen A. DeLurgio
title_short	Forecasting principles and applications
title_sort	forecasting principles and applications
topic	Mathematisches Modell Forecasting -- Mathematical models Forecasting -- Statistical methods Prognoseverfahren (DE-588)4358095-6 gnd
topic_facet	Mathematisches Modell Forecasting -- Mathematical models Forecasting -- Statistical methods Prognoseverfahren Lehrbuch
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007894024&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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